In [33]:
visualisation(train_ds_basic,5,'(basic)','train')
# !unzip 1.zip
%pip install tensorflow_addons keras-tuner pandas matplotlib seaborn scikit-learn tqdm efficientnet numba
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WARNING: You are using pip version 20.2.4; however, version 23.3.1 is available.
You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.
# !sudo nvidia-smi --gpu-reset -i 0
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from sklearn.metrics import classification_report, roc_auc_score, average_precision_score, confusion_matrix, cohen_kappa_score, matthews_corrcoef
from sklearn.metrics import classification_report,accuracy_score, confusion_matrix
from sklearn.decomposition import PCA
from sklearn.preprocessing import Normalizer
from sklearn.metrics import classification_report, accuracy_score
import os, time, math, datetime, warnings, pytz, glob
from IPython.display import display
from functools import reduce
import absl.logging
from tqdm import tqdm
import logging
from efficientnet.tfkeras import EfficientNetB3
absl.logging.set_verbosity(absl.logging.ERROR)
logging.getLogger('tensorflow').disabled = True
warnings.filterwarnings('ignore')
import tensorflow as tf
from tensorflow.keras.utils import Sequence, to_categorical
from tensorflow import expand_dims
from tensorflow.keras import Sequential
from tensorflow.keras import layers as L
from tensorflow.keras import backend as K
from tensorflow.image import random_flip_left_right, random_crop, resize_with_crop_or_pad
from tensorflow.keras.utils import to_categorical, plot_model
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.layers import (Dense, Input, InputLayer, Normalization, Flatten,BatchNormalization,
Dropout,Conv2D, GlobalAveragePooling2D, MaxPooling2D, ReLU, Layer,Activation, Multiply, AveragePooling2D,
Add, RandomRotation,Resizing, Rescaling, Reshape, Concatenate, concatenate, Lambda,LeakyReLU, ZeroPadding2D)
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, LearningRateScheduler, ReduceLROnPlateau, TerminateOnNaN, TensorBoard, CSVLogger, Callback
from tensorflow.keras.backend import clear_session
from tensorflow.keras.optimizers import RMSprop, SGD, Adam, Adagrad, Adamax
from tensorflow.keras.regularizers import l2, L2
from tensorflow.keras.optimizers.schedules import *
from tensorflow.keras.metrics import FalseNegatives, categorical_crossentropy, sparse_categorical_crossentropy
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.image import *
from tensorflow_addons.optimizers import SWA
from tensorflow.keras import layers
from kerastuner.tuners import Hyperband
from kerastuner import RandomSearch
from kerastuner import HyperModel
# Setting a seaborn style
sns.set(style="whitegrid")
seed = 32
tf.random.set_seed(seed)
np.random.seed(seed)
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(f"{len(gpus)} Physical GPUs, {len(logical_gpus)} Logical GPU")
except RuntimeError as e:
print(e)
1 Physical GPUs, 1 Logical GPU
!nvidia-smi
Sun Nov 26 16:01:29 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.129.03 Driver Version: 535.129.03 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 4090 On | 00000000:03:00.0 Off | Off |
| 0% 35C P2 51W / 450W | 5471MiB / 24564MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
+---------------------------------------------------------------------------------------+
data_big = tf.keras.utils.image_dataset_from_directory('Dataset for CA1 part A/train' ,
color_mode='rgb',
image_size=(128,128))
data_big
Found 9028 files belonging to 15 classes.
<BatchDataset element_spec=(TensorSpec(shape=(None, 128, 128, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>
X_train_big = []
y_train_big = []
for images, labels in tqdm(data_big):
images = tf.image.rgb_to_grayscale(images)
X_train_big.append(images)
y_train_big.append(labels)
X_train_big = np.concatenate(X_train_big, axis=0)
X_train_big = np.squeeze(X_train_big, axis=-1)
y_train_big = np.concatenate(y_train_big, axis=0)
100%|██████████| 283/283 [00:02<00:00, 116.51it/s]
val_data_big = tf.keras.utils.image_dataset_from_directory('Dataset for CA1 part A/validation' ,
color_mode='rgb',
image_size=(128,128))
val_data_big
Found 3000 files belonging to 15 classes.
<BatchDataset element_spec=(TensorSpec(shape=(None, 128, 128, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>
X_val_big = []
y_val_big = []
for images, labels in tqdm(val_data_big):
images = tf.image.rgb_to_grayscale(images)
X_val_big.append(images)
y_val_big.append(labels)
X_val_big = np.concatenate(X_val_big, axis=0)
X_val_big = np.squeeze(X_val_big, axis=-1)
y_val_big = np.concatenate(y_val_big, axis=0)
100%|██████████| 94/94 [00:00<00:00, 170.01it/s]
test_data_big = tf.keras.utils.image_dataset_from_directory('Dataset for CA1 part A/test' ,
color_mode='rgb',
image_size=(128,128))
test_data_big
Found 3000 files belonging to 15 classes.
<BatchDataset element_spec=(TensorSpec(shape=(None, 128, 128, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>
X_test_big = []
y_test_big = []
for images, labels in tqdm(test_data_big):
images = tf.image.rgb_to_grayscale(images)
X_test_big.append(images)
y_test_big.append(labels)
X_test_big = np.concatenate(X_test_big, axis=0)
X_test_big = np.squeeze(X_test_big, axis=-1)
y_test_big = np.concatenate(y_test_big, axis=0)
100%|██████████| 94/94 [00:00<00:00, 167.22it/s]
labels_dict = os.listdir('Dataset for CA1 part A/train')
labels_dict = {idx: label for idx, label in enumerate(labels_dict)}
print(labels_dict)
{0: 'Bean', 1: 'Bitter_Gourd', 2: 'Bottle_Gourd', 3: 'Brinjal', 4: 'Broccoli', 5: 'Cabbage', 6: 'Capsicum', 7: 'Carrot', 8: 'Cauliflower', 9: 'Cucumber', 10: 'Papaya', 11: 'Potato', 12: 'Pumpkin', 13: 'Radish', 14: 'Tomato'}
LR = 0.01
MOMENTUM = 0.9
WEIGHT_DECAY = 0.0005
val_split = 0.2
max_epochs = 100
y_train_big = to_categorical(y_train_big)
y_val_big = to_categorical(y_val_big)
def vgg_conv_layer(filters, kernel_size=3, activation='relu', weight_decay=WEIGHT_DECAY):
return Sequential([
Conv2D(filters, kernel_size, padding='same', activation=None, kernel_regularizer=l2(weight_decay)),
BatchNormalization(),
ReLU() if activation == 'relu' else activation
])
def vgg_conv_block(no_layers, filters, activation='relu'):
block = Sequential()
for _ in range(no_layers):
block.add(vgg_conv_layer(filters, activation=activation))
block.add(MaxPooling2D(pool_size=(2, 2), strides=2))
return block
class VGGNet(Model):
def __init__(self, input_shape, num_classes, name='VGGNet_Baseline'):
super(VGGNet, self).__init__(name=name)
self.vgg_blocks = [
vgg_conv_block(2, 32, activation='relu'),
vgg_conv_block(3, 64, activation='relu'),
vgg_conv_block(3, 128, activation='relu'),
vgg_conv_block(3, 256, activation='relu'),
]
self.global_pool = GlobalAveragePooling2D()
self.classifier = Sequential([
Dropout(0.3),
Dense(num_classes, activation='softmax')
])
def call(self, inputs):
x = inputs
for block in self.vgg_blocks:
x = block(x)
x = self.global_pool(x)
return self.classifier(x)
def build(self, input_shape):
inputs = Input(shape=(128,128,1))
model = Model(inputs=inputs, outputs=self.call(inputs), name=self.name)
return model
input_shape = (128, 128, 1)
num_classes = 15
vgg = VGGNet(input_shape, num_classes)
vgg_model = vgg.build(input_shape)
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
vgg_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
def CNN2(name='CNN2_Baseline'):
filters_list = [32, 64, 128, 128, 128, 128]
model = Sequential()
# Add initial convolutional layers
model.add(Conv2D(filters=filters_list[0], kernel_size=(3, 3), padding='same', input_shape=(128, 128, 1)))
model.add(BatchNormalization())
model.add(ReLU())
model.add(Conv2D(filters=filters_list[1], kernel_size=(3, 3), padding='same'))
model.add(BatchNormalization())
model.add(ReLU())
for filters in filters_list[2:]:
model.add(Conv2D(filters=filters, kernel_size=(3, 3), padding='same'))
model.add(BatchNormalization())
model.add(ReLU())
model.add(GlobalAveragePooling2D())
model.add(Dense(256, activation='relu'))
model.add(Dense(15, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
return model
no = lambda x : x
def data_before_train(self, aug_func=no):
train_ds = tf.data.Dataset.from_tensor_slices((X_train_big, y_train_big)).map(lambda x, y : (expand_dims(x, -1), y))
val_ds = tf.data.Dataset.from_tensor_slices((X_val_big, y_val_big)).shuffle(128 * 100).batch(128).map(lambda x, y : (expand_dims(x, -1), y)).prefetch(tf.data.AUTOTUNE)
train_ds = train_ds.map(lambda x, y : (aug_func(x), y), num_parallel_calls=tf.data.AUTOTUNE).shuffle(128 * 100).batch(128).prefetch(tf.data.AUTOTUNE)
return train_ds, val_ds
train_ds, val_ds = data_before_train(no)
def evaluation_test(model, X_val, y_val, LABELS):
final_predictions = model.predict(X_val)
final_predictions_final = np.argmax(final_predictions, axis=1)
y_test_final = np.argmax(y_val, axis=1) if y_val.ndim == 2 else y_val
classification_results = classification_report(y_test_final, final_predictions_final, target_names=LABELS.values(), output_dict=True)
print('Accuracy:', accuracy_score(y_test_final, final_predictions_final))
result = pd.DataFrame(classification_results).transpose().sort_values('f1-score')
return result
def plot_classification_heatmap(df):
heatmap_data = df.drop(['accuracy', 'macro avg', 'weighted avg']).iloc[:, :-1]
plt.figure(figsize=(10, 8))
sns.heatmap(heatmap_data, annot=True, fmt=".2f", cmap="Blues")
plt.title("Classification Report Heatmap")
plt.show()
class Evaluator:
def __init__(self, path=None, project_name="CNN_CA1"):
# Initialize wandb
self.project_name = project_name
# Your existing initialization code
if path:
self.result = pd.read_csv(path, sep=';')
else:
cols = ['Model Name', 'Batch Size', "Train Loss", "Test Loss", "Train Acc", "Test Acc", "Remarks"]
self.result = pd.DataFrame(columns=cols)
self.callback = [
TerminateOnNaN()
]
# self.api = wandb.Api()
def train_model(self, model, train, val, hyperparameters, callbacks):
tf.keras.backend.clear_session()
epochs = hyperparameters['max_epochs']
batch_size = 64
if val is None:
X_train, y_train = train
return model.fit(X_train, y_train, epochs=epochs, batch_size=64, validation_split = hyperparameters["val_split"], callbacks=callbacks).history
else:
return model.fit(train, validation_data = val, epochs = epochs, batch_size = batch_size , callbacks=callbacks).history
def model_evaluate(self, train, val, model, hyperparameters, callbacks=None, plot_loss=True, remarks=""):
tf.keras.backend.clear_session()
# wandb.init(project=self.project_name)
callbacks = (callbacks or [EarlyStopping(monitor='val_accuracy', patience=10, restore_best_weights=True), ReduceLROnPlateau(patience=5)]) + self.callback
history = self.train_model(model, train, val, hyperparameters, callbacks)
bestval_index = np.argmax(history['val_accuracy'])
if plot_loss:
try:
fig = plot_model_history(history)
except Exception as e:
print(e, 'here')
print("error creating loss curve")
fig = None
result = {
"Model Name": model.name,
"Epochs": len(history["loss"]),
"Batch Size": hyperparameters["batch_size"],
"Train Loss": history["loss"][bestval_index],
"Test Loss": history["val_loss"][bestval_index],
"Train Acc": history["accuracy"][bestval_index],
"Test Acc": history["val_accuracy"][bestval_index]
}
# wandb.log({
# "Model Name": model.name,
# "Epochs": len(history["loss"]),
# "Batch Size": hyperparameters["batch_size"],
# "Train Loss": history["loss"][bestval_index],
# "Test Loss": history["val_loss"][bestval_index],
# "Train Acc": history["accuracy"][bestval_index],
# "Test Acc": history["val_accuracy"][bestval_index],
# })
# wandb.finish()
return result, fig
def _train_model(self, model, training_data, validation_data, hyperparameters, callbacks):
tf.keras.backend.clear_session()
epochs = hyperparameters["max_epochs"]
batch_size = hyperparameters["batch_size"]
if validation_data is None:
X_train, y_train = training_data
return model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_split=hyperparameters["val_split"], callbacks=callbacks).history
return model.fit(training_data, validation_data=validation_data, epochs=epochs, batch_size=batch_size, callbacks=callbacks).history
def return_history(self, project_name='CNN_CA1', entity=None, include_cols=None):
if include_cols is None:
include_cols = ['Model Name', 'Epochs', 'Batch Size', 'Train Loss','Test Loss','Train Acc','Test Acc','Remarks']
runs = self.api.runs('111da/CNN_CA1')
# List to store results
results = []
for run in runs:
run_summary = run.summary
data = {}
for col in include_cols:
data[col] = run_summary.get(col, None)
results.append(data)
df = pd.DataFrame(results)
return df
def remove_model(self, model_name):
self.result = self.result[self.result["Model Name"] != model_name]
def plot_model_history(model_history):
history_df = pd.DataFrame(model_history)
fig, axs = plt.subplots(1, 2, figsize=(14, 5))
axs[0].plot(history_df['loss'], 'g--', label='Training Loss')
axs[0].plot(history_df['val_loss'], 'b-', label='Validation Loss')
axs[0].set_title('Training and Validation Loss')
axs[0].set_xlabel('Epochs')
axs[0].set_ylabel('Loss')
axs[0].legend()
axs[0].grid(True)
if 'accuracy' in history_df and 'val_accuracy' in history_df:
axs[1].plot(history_df['accuracy'], 'g--', label='Training Accuracy')
axs[1].plot(history_df['val_accuracy'], 'b-', label='Validation Accuracy')
axs[1].set_title('Training and Validation Accuracy')
axs[1].set_xlabel('Epochs')
axs[1].set_ylabel('Accuracy')
axs[1].legend()
axs[1].grid(True)
plt.tight_layout()
plt.close(fig)
def show():
display(fig)
return show
evaluator = Evaluator()
columns = ["Model Name", "Epochs", "Batch Size", "Train Loss", "Test Loss", "Train Acc", "Test Acc",'Kappa', "Comments"]
overall = pd.DataFrame(columns=columns)
print(overall)
Empty DataFrame Columns: [Model Name, Epochs, Batch Size, Train Loss, Test Loss, Train Acc, Test Acc, Kappa, Comments] Index: []
base_hparams = {"val_split" : val_split, "max_epochs" : max_epochs,"batch_size" : 128}
steps_per_epoch = np.ceil(len(X_train_big) / 128)
steps_per_epoch
71.0
def models_array():
def baseline_model2(optimizer='adam', name='CNN_Baseline'):
model = Sequential(name=name)
# Input layer
model.add(Input(shape=(128, 128, 1)))
# First Convolutional Block
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
# Second Convolutional Block
model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
# Third Convolutional Block
model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.4))
model.add(Flatten())
# Dense Block
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
# Dense Block
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
# Output Layer
model.add(Dense(15, activation='softmax'))
# Compile the model
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
return model
input_shape = (128, 128, 1)
num_classes = 15
vgg = VGGNet(input_shape, num_classes)
vgg_model = vgg.build(input_shape)
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
vgg_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
cnn2 = CNN2()
cnn = baseline_model2()
models = [cnn, cnn2, vgg_model ]
return models
model_names = ['CNN','CNN2','VGG_Baseline']
def data_augmentation_1(image):
image = tf.image.random_flip_left_right(image)
image = tf.image.random_flip_up_down(image)
image = tf.image.rot90(image, k=tf.random.uniform(shape=[], minval=0, maxval=4, dtype=tf.int32))
return image
def preprocess2(image, label):
if len(image.shape) == 2:
image = tf.expand_dims(image, axis=-1)
image = data_augmentation_1(image)
return image, label
train_ds_basic = tf.data.Dataset.from_tensor_slices((X_train_big, y_train_big))
train_ds_basic = train_ds_basic.map(preprocess2).batch(128).prefetch(tf.data.AUTOTUNE)
val_ds_basic = tf.data.Dataset.from_tensor_slices((X_val_big, y_val_big))
val_ds_basic = val_ds_basic.map(preprocess2).batch(128).prefetch(tf.data.AUTOTUNE)
def visualisation(dataset, num_images, type, type2):
plt.figure(figsize=(num_images * 3, 3))
plt.suptitle(f'Image Visualization {type} on {type2} dataset')
for images, labels in dataset.take(1):
for i in range(num_images):
ax = plt.subplot(1, num_images, i + 1)
plt.imshow(images[i].numpy().squeeze(), cmap='gray')
plt.axis("off")
plt.show()
visualisation(train_ds_basic,5,'(basic)','train')
figures = []
models = models_array()
for i in range(len(models)):
print(f'Running {model_names[i]}')
results, fig = evaluator.model_evaluate( train_ds_basic, val_ds_basic , models[i], base_hparams)
results['Model Name'] = f'{model_names[i]} basic one'
y_pred = models[i].predict(X_test_big)
y_pred_classes = np.argmax(y_pred, axis=1)
kappa = cohen_kappa_score(y_test_big, y_pred_classes)
print("Cohen’s Kappa Score:", kappa)
results['Kappa'] = kappa
overall = pd.concat([overall, pd.DataFrame([results])], ignore_index=True)
figures.append(fig)
Model: "sequential_32"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_22 (Conv2D) (None, 128, 128, 32) 320
batch_normalization_22 (Bat (None, 128, 128, 32) 128
chNormalization)
re_lu_22 (ReLU) (None, 128, 128, 32) 0
conv2d_23 (Conv2D) (None, 128, 128, 64) 18496
batch_normalization_23 (Bat (None, 128, 128, 64) 256
chNormalization)
re_lu_23 (ReLU) (None, 128, 128, 64) 0
conv2d_24 (Conv2D) (None, 128, 128, 128) 73856
batch_normalization_24 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_24 (ReLU) (None, 128, 128, 128) 0
conv2d_25 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_25 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_25 (ReLU) (None, 128, 128, 128) 0
conv2d_26 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_26 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_26 (ReLU) (None, 128, 128, 128) 0
conv2d_27 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_27 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_27 (ReLU) (None, 128, 128, 128) 0
global_average_pooling2d_2 (None, 128) 0
(GlobalAveragePooling2D)
dense_2 (Dense) (None, 256) 33024
dense_3 (Dense) (None, 15) 3855
=================================================================
Total params: 574,735
Trainable params: 573,519
Non-trainable params: 1,216
_________________________________________________________________
Model: "CNN_Baseline"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_28 (Conv2D) (None, 128, 128, 64) 640
batch_normalization_28 (Bat (None, 128, 128, 64) 256
chNormalization)
max_pooling2d_8 (MaxPooling (None, 64, 64, 64) 0
2D)
dropout_2 (Dropout) (None, 64, 64, 64) 0
conv2d_29 (Conv2D) (None, 64, 64, 128) 73856
batch_normalization_29 (Bat (None, 64, 64, 128) 512
chNormalization)
max_pooling2d_9 (MaxPooling (None, 32, 32, 128) 0
2D)
dropout_3 (Dropout) (None, 32, 32, 128) 0
conv2d_30 (Conv2D) (None, 32, 32, 256) 295168
batch_normalization_30 (Bat (None, 32, 32, 256) 1024
chNormalization)
max_pooling2d_10 (MaxPoolin (None, 16, 16, 256) 0
g2D)
dropout_4 (Dropout) (None, 16, 16, 256) 0
flatten (Flatten) (None, 65536) 0
dense_4 (Dense) (None, 512) 33554944
batch_normalization_31 (Bat (None, 512) 2048
chNormalization)
dropout_5 (Dropout) (None, 512) 0
dense_5 (Dense) (None, 128) 65664
batch_normalization_32 (Bat (None, 128) 512
chNormalization)
dropout_6 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 15) 1935
=================================================================
Total params: 33,996,559
Trainable params: 33,994,383
Non-trainable params: 2,176
_________________________________________________________________
Running CNN
Epoch 1/100
71/71 [==============================] - 10s 90ms/step - loss: 2.7465 - accuracy: 0.2442 - val_loss: 5.1188 - val_accuracy: 0.0987 - lr: 0.0010
Epoch 2/100
71/71 [==============================] - 5s 76ms/step - loss: 1.7977 - accuracy: 0.4373 - val_loss: 3.6735 - val_accuracy: 0.1540 - lr: 0.0010
Epoch 3/100
71/71 [==============================] - 5s 77ms/step - loss: 1.2818 - accuracy: 0.5784 - val_loss: 4.6390 - val_accuracy: 0.1230 - lr: 0.0010
Epoch 4/100
71/71 [==============================] - 5s 77ms/step - loss: 1.0103 - accuracy: 0.6716 - val_loss: 2.3952 - val_accuracy: 0.3440 - lr: 0.0010
Epoch 5/100
71/71 [==============================] - 5s 74ms/step - loss: 0.7940 - accuracy: 0.7384 - val_loss: 3.7721 - val_accuracy: 0.2637 - lr: 0.0010
Epoch 6/100
71/71 [==============================] - 5s 74ms/step - loss: 0.6396 - accuracy: 0.7940 - val_loss: 1.5061 - val_accuracy: 0.5107 - lr: 0.0010
Epoch 7/100
71/71 [==============================] - 5s 77ms/step - loss: 0.4792 - accuracy: 0.8571 - val_loss: 1.3530 - val_accuracy: 0.5743 - lr: 0.0010
Epoch 8/100
71/71 [==============================] - 5s 77ms/step - loss: 0.3731 - accuracy: 0.8841 - val_loss: 1.3919 - val_accuracy: 0.5947 - lr: 0.0010
Epoch 9/100
71/71 [==============================] - 5s 74ms/step - loss: 0.2876 - accuracy: 0.9157 - val_loss: 1.9526 - val_accuracy: 0.5160 - lr: 0.0010
Epoch 10/100
71/71 [==============================] - 5s 77ms/step - loss: 0.2515 - accuracy: 0.9233 - val_loss: 1.7421 - val_accuracy: 0.5953 - lr: 0.0010
Epoch 11/100
71/71 [==============================] - 6s 79ms/step - loss: 0.1896 - accuracy: 0.9456 - val_loss: 1.1901 - val_accuracy: 0.6790 - lr: 0.0010
Epoch 12/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1421 - accuracy: 0.9595 - val_loss: 3.5179 - val_accuracy: 0.4060 - lr: 0.0010
Epoch 13/100
71/71 [==============================] - 6s 78ms/step - loss: 0.1243 - accuracy: 0.9631 - val_loss: 0.9306 - val_accuracy: 0.7537 - lr: 0.0010
Epoch 14/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1573 - accuracy: 0.9553 - val_loss: 3.1776 - val_accuracy: 0.4430 - lr: 0.0010
Epoch 15/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1530 - accuracy: 0.9539 - val_loss: 3.5593 - val_accuracy: 0.4663 - lr: 0.0010
Epoch 16/100
71/71 [==============================] - 5s 76ms/step - loss: 0.0943 - accuracy: 0.9720 - val_loss: 2.5667 - val_accuracy: 0.5480 - lr: 0.0010
Epoch 17/100
71/71 [==============================] - 6s 79ms/step - loss: 0.0648 - accuracy: 0.9822 - val_loss: 0.7054 - val_accuracy: 0.8047 - lr: 0.0010
Epoch 18/100
71/71 [==============================] - 6s 78ms/step - loss: 0.0548 - accuracy: 0.9859 - val_loss: 0.7096 - val_accuracy: 0.7953 - lr: 0.0010
Epoch 19/100
71/71 [==============================] - 5s 76ms/step - loss: 0.0477 - accuracy: 0.9878 - val_loss: 2.1180 - val_accuracy: 0.5820 - lr: 0.0010
Epoch 20/100
71/71 [==============================] - 5s 72ms/step - loss: 0.0378 - accuracy: 0.9899 - val_loss: 2.0568 - val_accuracy: 0.6080 - lr: 0.0010
Epoch 21/100
71/71 [==============================] - 5s 75ms/step - loss: 0.0498 - accuracy: 0.9848 - val_loss: 1.6219 - val_accuracy: 0.6700 - lr: 0.0010
Epoch 22/100
71/71 [==============================] - 5s 75ms/step - loss: 0.0375 - accuracy: 0.9897 - val_loss: 1.4325 - val_accuracy: 0.6947 - lr: 0.0010
Epoch 23/100
71/71 [==============================] - 5s 74ms/step - loss: 0.0320 - accuracy: 0.9915 - val_loss: 0.6269 - val_accuracy: 0.8293 - lr: 1.0000e-04
Epoch 24/100
71/71 [==============================] - 5s 75ms/step - loss: 0.0252 - accuracy: 0.9944 - val_loss: 0.6727 - val_accuracy: 0.8273 - lr: 1.0000e-04
Epoch 25/100
71/71 [==============================] - 5s 76ms/step - loss: 0.0246 - accuracy: 0.9945 - val_loss: 0.8069 - val_accuracy: 0.8067 - lr: 1.0000e-04
Epoch 26/100
71/71 [==============================] - 5s 76ms/step - loss: 0.0237 - accuracy: 0.9940 - val_loss: 0.5632 - val_accuracy: 0.8397 - lr: 1.0000e-04
Epoch 27/100
71/71 [==============================] - 5s 75ms/step - loss: 0.0219 - accuracy: 0.9952 - val_loss: 0.6007 - val_accuracy: 0.8380 - lr: 1.0000e-04
Epoch 28/100
71/71 [==============================] - 5s 74ms/step - loss: 0.0179 - accuracy: 0.9967 - val_loss: 0.6234 - val_accuracy: 0.8400 - lr: 1.0000e-04
Epoch 29/100
71/71 [==============================] - 5s 77ms/step - loss: 0.0184 - accuracy: 0.9965 - val_loss: 0.6709 - val_accuracy: 0.8303 - lr: 1.0000e-04
Epoch 30/100
71/71 [==============================] - 5s 75ms/step - loss: 0.0162 - accuracy: 0.9965 - val_loss: 0.7948 - val_accuracy: 0.8030 - lr: 1.0000e-04
Epoch 31/100
71/71 [==============================] - 5s 75ms/step - loss: 0.0182 - accuracy: 0.9949 - val_loss: 0.5968 - val_accuracy: 0.8400 - lr: 1.0000e-04
Epoch 32/100
71/71 [==============================] - 5s 74ms/step - loss: 0.0142 - accuracy: 0.9976 - val_loss: 0.6054 - val_accuracy: 0.8390 - lr: 1.0000e-05
Epoch 33/100
71/71 [==============================] - 5s 75ms/step - loss: 0.0155 - accuracy: 0.9970 - val_loss: 0.6214 - val_accuracy: 0.8367 - lr: 1.0000e-05
Epoch 34/100
71/71 [==============================] - 5s 73ms/step - loss: 0.0160 - accuracy: 0.9971 - val_loss: 0.6688 - val_accuracy: 0.8297 - lr: 1.0000e-05
Epoch 35/100
71/71 [==============================] - 5s 73ms/step - loss: 0.0144 - accuracy: 0.9976 - val_loss: 0.6371 - val_accuracy: 0.8337 - lr: 1.0000e-05
Epoch 36/100
71/71 [==============================] - 5s 76ms/step - loss: 0.0149 - accuracy: 0.9978 - val_loss: 0.6413 - val_accuracy: 0.8340 - lr: 1.0000e-05
Epoch 37/100
71/71 [==============================] - 5s 74ms/step - loss: 0.0139 - accuracy: 0.9980 - val_loss: 0.6445 - val_accuracy: 0.8340 - lr: 1.0000e-06
Epoch 38/100
71/71 [==============================] - 5s 74ms/step - loss: 0.0154 - accuracy: 0.9969 - val_loss: 0.6449 - val_accuracy: 0.8347 - lr: 1.0000e-06
94/94 [==============================] - 1s 5ms/step
Cohen’s Kappa Score: 0.8392857142857143
Running CNN2
Epoch 1/100
71/71 [==============================] - 21s 270ms/step - loss: 1.7824 - accuracy: 0.4523 - val_loss: 5.1106 - val_accuracy: 0.0773 - lr: 0.0010
Epoch 2/100
71/71 [==============================] - 18s 254ms/step - loss: 1.1809 - accuracy: 0.6377 - val_loss: 2.5932 - val_accuracy: 0.3307 - lr: 0.0010
Epoch 3/100
71/71 [==============================] - 18s 254ms/step - loss: 0.8713 - accuracy: 0.7387 - val_loss: 3.0263 - val_accuracy: 0.2913 - lr: 0.0010
Epoch 4/100
71/71 [==============================] - 18s 254ms/step - loss: 0.6888 - accuracy: 0.7880 - val_loss: 6.6438 - val_accuracy: 0.2480 - lr: 0.0010
Epoch 5/100
71/71 [==============================] - 18s 255ms/step - loss: 0.5522 - accuracy: 0.8343 - val_loss: 6.6184 - val_accuracy: 0.2293 - lr: 0.0010
Epoch 6/100
71/71 [==============================] - 18s 255ms/step - loss: 0.4529 - accuracy: 0.8652 - val_loss: 2.9774 - val_accuracy: 0.5367 - lr: 0.0010
Epoch 7/100
71/71 [==============================] - 18s 255ms/step - loss: 0.3810 - accuracy: 0.8871 - val_loss: 2.0030 - val_accuracy: 0.5687 - lr: 0.0010
Epoch 8/100
71/71 [==============================] - 18s 255ms/step - loss: 0.3258 - accuracy: 0.9024 - val_loss: 4.0875 - val_accuracy: 0.3987 - lr: 0.0010
Epoch 9/100
71/71 [==============================] - 18s 255ms/step - loss: 0.2842 - accuracy: 0.9153 - val_loss: 4.3294 - val_accuracy: 0.4397 - lr: 0.0010
Epoch 10/100
71/71 [==============================] - 18s 255ms/step - loss: 0.2552 - accuracy: 0.9231 - val_loss: 1.6425 - val_accuracy: 0.5740 - lr: 0.0010
Epoch 11/100
71/71 [==============================] - 18s 255ms/step - loss: 0.2200 - accuracy: 0.9363 - val_loss: 2.6448 - val_accuracy: 0.5767 - lr: 0.0010
Epoch 12/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1936 - accuracy: 0.9445 - val_loss: 5.4075 - val_accuracy: 0.3933 - lr: 0.0010
Epoch 13/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1754 - accuracy: 0.9516 - val_loss: 0.8237 - val_accuracy: 0.7467 - lr: 0.0010
Epoch 14/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1548 - accuracy: 0.9561 - val_loss: 1.8084 - val_accuracy: 0.5583 - lr: 0.0010
Epoch 15/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1401 - accuracy: 0.9605 - val_loss: 0.5111 - val_accuracy: 0.8337 - lr: 0.0010
Epoch 16/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1276 - accuracy: 0.9647 - val_loss: 0.8670 - val_accuracy: 0.7673 - lr: 0.0010
Epoch 17/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1178 - accuracy: 0.9691 - val_loss: 0.3603 - val_accuracy: 0.8790 - lr: 0.0010
Epoch 18/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1050 - accuracy: 0.9713 - val_loss: 0.7675 - val_accuracy: 0.7690 - lr: 0.0010
Epoch 19/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1014 - accuracy: 0.9726 - val_loss: 1.3476 - val_accuracy: 0.7067 - lr: 0.0010
Epoch 20/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0888 - accuracy: 0.9773 - val_loss: 0.9765 - val_accuracy: 0.7357 - lr: 0.0010
Epoch 21/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0844 - accuracy: 0.9790 - val_loss: 1.3821 - val_accuracy: 0.6623 - lr: 0.0010
Epoch 22/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0823 - accuracy: 0.9795 - val_loss: 3.4315 - val_accuracy: 0.4403 - lr: 0.0010
Epoch 23/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0519 - accuracy: 0.9877 - val_loss: 0.2091 - val_accuracy: 0.9337 - lr: 1.0000e-04
Epoch 24/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0360 - accuracy: 0.9945 - val_loss: 0.1241 - val_accuracy: 0.9643 - lr: 1.0000e-04
Epoch 25/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0325 - accuracy: 0.9950 - val_loss: 0.1048 - val_accuracy: 0.9710 - lr: 1.0000e-04
Epoch 26/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0303 - accuracy: 0.9955 - val_loss: 0.0962 - val_accuracy: 0.9733 - lr: 1.0000e-04
Epoch 27/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0287 - accuracy: 0.9960 - val_loss: 0.0924 - val_accuracy: 0.9750 - lr: 1.0000e-04
Epoch 28/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0273 - accuracy: 0.9965 - val_loss: 0.0896 - val_accuracy: 0.9763 - lr: 1.0000e-04
Epoch 29/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0262 - accuracy: 0.9968 - val_loss: 0.0883 - val_accuracy: 0.9763 - lr: 1.0000e-04
Epoch 30/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0252 - accuracy: 0.9975 - val_loss: 0.0874 - val_accuracy: 0.9767 - lr: 1.0000e-04
Epoch 31/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0242 - accuracy: 0.9977 - val_loss: 0.0847 - val_accuracy: 0.9773 - lr: 1.0000e-04
Epoch 32/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0234 - accuracy: 0.9980 - val_loss: 0.0831 - val_accuracy: 0.9770 - lr: 1.0000e-04
Epoch 33/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0226 - accuracy: 0.9980 - val_loss: 0.0819 - val_accuracy: 0.9773 - lr: 1.0000e-04
Epoch 34/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0218 - accuracy: 0.9981 - val_loss: 0.0806 - val_accuracy: 0.9773 - lr: 1.0000e-04
Epoch 35/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0211 - accuracy: 0.9983 - val_loss: 0.0803 - val_accuracy: 0.9773 - lr: 1.0000e-04
Epoch 36/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0204 - accuracy: 0.9986 - val_loss: 0.0814 - val_accuracy: 0.9777 - lr: 1.0000e-04
Epoch 37/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0198 - accuracy: 0.9987 - val_loss: 0.0809 - val_accuracy: 0.9770 - lr: 1.0000e-04
Epoch 38/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0192 - accuracy: 0.9989 - val_loss: 0.0810 - val_accuracy: 0.9767 - lr: 1.0000e-04
Epoch 39/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0186 - accuracy: 0.9991 - val_loss: 0.0804 - val_accuracy: 0.9763 - lr: 1.0000e-04
Epoch 40/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0180 - accuracy: 0.9991 - val_loss: 0.0805 - val_accuracy: 0.9770 - lr: 1.0000e-04
Epoch 41/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0163 - accuracy: 0.9997 - val_loss: 0.0645 - val_accuracy: 0.9817 - lr: 1.0000e-05
Epoch 42/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0158 - accuracy: 0.9997 - val_loss: 0.0630 - val_accuracy: 0.9823 - lr: 1.0000e-05
Epoch 43/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0156 - accuracy: 0.9997 - val_loss: 0.0623 - val_accuracy: 0.9827 - lr: 1.0000e-05
Epoch 44/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0155 - accuracy: 0.9997 - val_loss: 0.0621 - val_accuracy: 0.9827 - lr: 1.0000e-05
Epoch 45/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0154 - accuracy: 0.9997 - val_loss: 0.0620 - val_accuracy: 0.9823 - lr: 1.0000e-05
Epoch 46/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0153 - accuracy: 0.9997 - val_loss: 0.0621 - val_accuracy: 0.9820 - lr: 1.0000e-05
Epoch 47/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0153 - accuracy: 0.9997 - val_loss: 0.0621 - val_accuracy: 0.9820 - lr: 1.0000e-05
Epoch 48/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0152 - accuracy: 0.9997 - val_loss: 0.0621 - val_accuracy: 0.9820 - lr: 1.0000e-05
Epoch 49/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0151 - accuracy: 0.9997 - val_loss: 0.0621 - val_accuracy: 0.9820 - lr: 1.0000e-05
Epoch 50/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0148 - accuracy: 0.9997 - val_loss: 0.0616 - val_accuracy: 0.9827 - lr: 1.0000e-06
Epoch 51/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0148 - accuracy: 0.9997 - val_loss: 0.0613 - val_accuracy: 0.9827 - lr: 1.0000e-06
Epoch 52/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0148 - accuracy: 0.9997 - val_loss: 0.0612 - val_accuracy: 0.9827 - lr: 1.0000e-06
Epoch 53/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0148 - accuracy: 0.9997 - val_loss: 0.0612 - val_accuracy: 0.9827 - lr: 1.0000e-06
94/94 [==============================] - 2s 18ms/step
Cohen’s Kappa Score: 0.985
Running VGG_Baseline
Epoch 1/100
71/71 [==============================] - 8s 88ms/step - loss: 3.6950 - accuracy: 0.2409 - val_loss: 68.6340 - val_accuracy: 0.0667 - lr: 0.0100
Epoch 2/100
71/71 [==============================] - 5s 75ms/step - loss: 2.4062 - accuracy: 0.3767 - val_loss: 12.7772 - val_accuracy: 0.0703 - lr: 0.0100
Epoch 3/100
71/71 [==============================] - 5s 75ms/step - loss: 2.2203 - accuracy: 0.4179 - val_loss: 5.5883 - val_accuracy: 0.0730 - lr: 0.0100
Epoch 4/100
71/71 [==============================] - 5s 76ms/step - loss: 2.0130 - accuracy: 0.4777 - val_loss: 6.7666 - val_accuracy: 0.1070 - lr: 0.0100
Epoch 5/100
71/71 [==============================] - 5s 77ms/step - loss: 1.9166 - accuracy: 0.5165 - val_loss: 3.4060 - val_accuracy: 0.1387 - lr: 0.0100
Epoch 6/100
71/71 [==============================] - 5s 75ms/step - loss: 1.8414 - accuracy: 0.5438 - val_loss: 3.8706 - val_accuracy: 0.1540 - lr: 0.0100
Epoch 7/100
71/71 [==============================] - 5s 76ms/step - loss: 1.7080 - accuracy: 0.5752 - val_loss: 7.4286 - val_accuracy: 0.0847 - lr: 0.0100
Epoch 8/100
71/71 [==============================] - 6s 78ms/step - loss: 1.6263 - accuracy: 0.5913 - val_loss: 3.1665 - val_accuracy: 0.1607 - lr: 0.0100
Epoch 9/100
71/71 [==============================] - 5s 73ms/step - loss: 1.5701 - accuracy: 0.6083 - val_loss: 3.0710 - val_accuracy: 0.1940 - lr: 0.0100
Epoch 10/100
71/71 [==============================] - 5s 75ms/step - loss: 1.5174 - accuracy: 0.6214 - val_loss: 3.5302 - val_accuracy: 0.2167 - lr: 0.0100
Epoch 11/100
71/71 [==============================] - 5s 77ms/step - loss: 1.4595 - accuracy: 0.6358 - val_loss: 3.2446 - val_accuracy: 0.2583 - lr: 0.0100
Epoch 12/100
71/71 [==============================] - 5s 76ms/step - loss: 1.5632 - accuracy: 0.6289 - val_loss: 5.4730 - val_accuracy: 0.1883 - lr: 0.0100
Epoch 13/100
71/71 [==============================] - 5s 76ms/step - loss: 1.4138 - accuracy: 0.6629 - val_loss: 3.0921 - val_accuracy: 0.2607 - lr: 0.0100
Epoch 14/100
71/71 [==============================] - 5s 73ms/step - loss: 1.3052 - accuracy: 0.6808 - val_loss: 3.6984 - val_accuracy: 0.2297 - lr: 0.0100
Epoch 15/100
71/71 [==============================] - 5s 76ms/step - loss: 1.0104 - accuracy: 0.7754 - val_loss: 2.1325 - val_accuracy: 0.3650 - lr: 1.0000e-03
Epoch 16/100
71/71 [==============================] - 5s 75ms/step - loss: 0.8821 - accuracy: 0.8048 - val_loss: 1.8328 - val_accuracy: 0.4193 - lr: 1.0000e-03
Epoch 17/100
71/71 [==============================] - 5s 75ms/step - loss: 0.8131 - accuracy: 0.8154 - val_loss: 1.9619 - val_accuracy: 0.3440 - lr: 1.0000e-03
Epoch 18/100
71/71 [==============================] - 5s 75ms/step - loss: 0.7454 - accuracy: 0.8334 - val_loss: 2.0232 - val_accuracy: 0.3390 - lr: 1.0000e-03
Epoch 19/100
71/71 [==============================] - 5s 76ms/step - loss: 0.6969 - accuracy: 0.8424 - val_loss: 1.7531 - val_accuracy: 0.4460 - lr: 1.0000e-03
Epoch 20/100
71/71 [==============================] - 5s 74ms/step - loss: 0.6519 - accuracy: 0.8588 - val_loss: 1.8676 - val_accuracy: 0.4400 - lr: 1.0000e-03
Epoch 21/100
71/71 [==============================] - 6s 77ms/step - loss: 0.5964 - accuracy: 0.8735 - val_loss: 1.4284 - val_accuracy: 0.5753 - lr: 1.0000e-03
Epoch 22/100
71/71 [==============================] - 5s 74ms/step - loss: 0.5669 - accuracy: 0.8831 - val_loss: 1.5772 - val_accuracy: 0.5330 - lr: 1.0000e-03
Epoch 23/100
71/71 [==============================] - 5s 76ms/step - loss: 0.5367 - accuracy: 0.8938 - val_loss: 1.5474 - val_accuracy: 0.5623 - lr: 1.0000e-03
Epoch 24/100
71/71 [==============================] - 5s 75ms/step - loss: 0.4984 - accuracy: 0.8999 - val_loss: 1.2087 - val_accuracy: 0.6750 - lr: 1.0000e-03
Epoch 25/100
71/71 [==============================] - 5s 75ms/step - loss: 0.4570 - accuracy: 0.9149 - val_loss: 1.0185 - val_accuracy: 0.7140 - lr: 1.0000e-03
Epoch 26/100
71/71 [==============================] - 5s 74ms/step - loss: 0.4353 - accuracy: 0.9207 - val_loss: 1.6481 - val_accuracy: 0.6143 - lr: 1.0000e-03
Epoch 27/100
71/71 [==============================] - 5s 75ms/step - loss: 0.4167 - accuracy: 0.9265 - val_loss: 1.1632 - val_accuracy: 0.6717 - lr: 1.0000e-03
Epoch 28/100
71/71 [==============================] - 5s 77ms/step - loss: 0.3879 - accuracy: 0.9349 - val_loss: 1.0086 - val_accuracy: 0.7383 - lr: 1.0000e-03
Epoch 29/100
71/71 [==============================] - 6s 78ms/step - loss: 0.3836 - accuracy: 0.9373 - val_loss: 0.7292 - val_accuracy: 0.8153 - lr: 1.0000e-03
Epoch 30/100
71/71 [==============================] - 5s 75ms/step - loss: 0.3672 - accuracy: 0.9374 - val_loss: 0.9760 - val_accuracy: 0.7563 - lr: 1.0000e-03
Epoch 31/100
71/71 [==============================] - 5s 74ms/step - loss: 0.3568 - accuracy: 0.9443 - val_loss: 0.9187 - val_accuracy: 0.7627 - lr: 1.0000e-03
Epoch 32/100
71/71 [==============================] - 5s 74ms/step - loss: 0.3317 - accuracy: 0.9513 - val_loss: 0.6257 - val_accuracy: 0.8483 - lr: 1.0000e-03
Epoch 33/100
71/71 [==============================] - 5s 73ms/step - loss: 0.3212 - accuracy: 0.9514 - val_loss: 0.5597 - val_accuracy: 0.8780 - lr: 1.0000e-03
Epoch 34/100
71/71 [==============================] - 5s 76ms/step - loss: 0.2989 - accuracy: 0.9591 - val_loss: 1.0661 - val_accuracy: 0.7450 - lr: 1.0000e-03
Epoch 35/100
71/71 [==============================] - 5s 74ms/step - loss: 0.3095 - accuracy: 0.9541 - val_loss: 0.6994 - val_accuracy: 0.8260 - lr: 1.0000e-03
Epoch 36/100
71/71 [==============================] - 5s 74ms/step - loss: 0.2983 - accuracy: 0.9580 - val_loss: 0.8261 - val_accuracy: 0.8040 - lr: 1.0000e-03
Epoch 37/100
71/71 [==============================] - 5s 75ms/step - loss: 0.2875 - accuracy: 0.9621 - val_loss: 0.8876 - val_accuracy: 0.7987 - lr: 1.0000e-03
Epoch 38/100
71/71 [==============================] - 5s 74ms/step - loss: 0.2918 - accuracy: 0.9584 - val_loss: 0.7718 - val_accuracy: 0.8107 - lr: 1.0000e-03
Epoch 39/100
71/71 [==============================] - 6s 77ms/step - loss: 0.2423 - accuracy: 0.9761 - val_loss: 0.3302 - val_accuracy: 0.9497 - lr: 1.0000e-04
Epoch 40/100
71/71 [==============================] - 5s 72ms/step - loss: 0.2138 - accuracy: 0.9866 - val_loss: 0.3020 - val_accuracy: 0.9590 - lr: 1.0000e-04
Epoch 41/100
71/71 [==============================] - 5s 76ms/step - loss: 0.2060 - accuracy: 0.9896 - val_loss: 0.2851 - val_accuracy: 0.9617 - lr: 1.0000e-04
Epoch 42/100
71/71 [==============================] - 5s 75ms/step - loss: 0.2014 - accuracy: 0.9891 - val_loss: 0.2725 - val_accuracy: 0.9650 - lr: 1.0000e-04
Epoch 43/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1979 - accuracy: 0.9898 - val_loss: 0.2774 - val_accuracy: 0.9650 - lr: 1.0000e-04
Epoch 44/100
71/71 [==============================] - 5s 77ms/step - loss: 0.1927 - accuracy: 0.9924 - val_loss: 0.2649 - val_accuracy: 0.9673 - lr: 1.0000e-04
Epoch 45/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1905 - accuracy: 0.9920 - val_loss: 0.2633 - val_accuracy: 0.9677 - lr: 1.0000e-04
Epoch 46/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1876 - accuracy: 0.9935 - val_loss: 0.2694 - val_accuracy: 0.9660 - lr: 1.0000e-04
Epoch 47/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1838 - accuracy: 0.9945 - val_loss: 0.2527 - val_accuracy: 0.9693 - lr: 1.0000e-04
Epoch 48/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1817 - accuracy: 0.9953 - val_loss: 0.2561 - val_accuracy: 0.9690 - lr: 1.0000e-04
Epoch 49/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1785 - accuracy: 0.9946 - val_loss: 0.2495 - val_accuracy: 0.9687 - lr: 1.0000e-04
Epoch 50/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1759 - accuracy: 0.9960 - val_loss: 0.2458 - val_accuracy: 0.9690 - lr: 1.0000e-04
Epoch 51/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1720 - accuracy: 0.9967 - val_loss: 0.2437 - val_accuracy: 0.9697 - lr: 1.0000e-04
Epoch 52/100
71/71 [==============================] - 5s 72ms/step - loss: 0.1718 - accuracy: 0.9959 - val_loss: 0.2506 - val_accuracy: 0.9693 - lr: 1.0000e-04
Epoch 53/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1697 - accuracy: 0.9968 - val_loss: 0.2402 - val_accuracy: 0.9740 - lr: 1.0000e-04
Epoch 54/100
71/71 [==============================] - 5s 73ms/step - loss: 0.1681 - accuracy: 0.9961 - val_loss: 0.2409 - val_accuracy: 0.9720 - lr: 1.0000e-04
Epoch 55/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1647 - accuracy: 0.9970 - val_loss: 0.2398 - val_accuracy: 0.9733 - lr: 1.0000e-04
Epoch 56/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1632 - accuracy: 0.9970 - val_loss: 0.2594 - val_accuracy: 0.9640 - lr: 1.0000e-04
Epoch 57/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1620 - accuracy: 0.9972 - val_loss: 0.2331 - val_accuracy: 0.9740 - lr: 1.0000e-04
Epoch 58/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1597 - accuracy: 0.9970 - val_loss: 0.2388 - val_accuracy: 0.9697 - lr: 1.0000e-04
Epoch 59/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1568 - accuracy: 0.9986 - val_loss: 0.2407 - val_accuracy: 0.9707 - lr: 1.0000e-04
Epoch 60/100
71/71 [==============================] - 5s 73ms/step - loss: 0.1549 - accuracy: 0.9977 - val_loss: 0.2338 - val_accuracy: 0.9727 - lr: 1.0000e-04
Epoch 61/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1531 - accuracy: 0.9981 - val_loss: 0.2322 - val_accuracy: 0.9723 - lr: 1.0000e-04
Epoch 62/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1516 - accuracy: 0.9981 - val_loss: 0.2278 - val_accuracy: 0.9737 - lr: 1.0000e-04
Epoch 63/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1503 - accuracy: 0.9977 - val_loss: 0.2352 - val_accuracy: 0.9713 - lr: 1.0000e-04
94/94 [==============================] - 1s 6ms/step
Cohen’s Kappa Score: 0.9725
overall.iloc[-3:]
| Model Name | Epochs | Batch Size | Train Loss | Test Loss | Train Acc | Test Acc | Kappa | Comments | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | CNN basic one | 38 | 128 | 0.017855 | 0.623409 | 0.996677 | 0.840000 | 0.839286 | NaN |
| 1 | CNN2 basic one | 53 | 128 | 0.015596 | 0.062343 | 0.999668 | 0.982667 | 0.985000 | NaN |
| 2 | VGG_Baseline basic one | 63 | 128 | 0.169651 | 0.240200 | 0.996788 | 0.974000 | 0.972500 | NaN |
for fig in figures:
fig()
def data_augmentation2(image):
image = tf.image.random_brightness(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
return image
train_ds_basic_2 = tf.data.Dataset.from_tensor_slices((X_train_big, y_train_big))
train_ds_basic_2 = train_ds_basic_2.map(preprocess2).batch(128).prefetch(tf.data.AUTOTUNE)
val_ds_basic_2 = tf.data.Dataset.from_tensor_slices((X_val_big, y_val_big))
val_ds_basic_2 = val_ds_basic_2.map(preprocess2).batch(128).prefetch(tf.data.AUTOTUNE)
visualisation(train_ds_basic_2,5,'(basic 2)','train')
figures = []
models = models_array()
for i in range(len(models)):
print(f'Running {model_names[i]}')
results, fig = evaluator.model_evaluate( train_ds_basic_2, val_ds_basic_2 , models[i], base_hparams)
results['Model Name'] = f'{model_names[i]} basic two'
y_pred = models[i].predict(X_test_big)
y_pred_classes = np.argmax(y_pred, axis=1)
kappa = cohen_kappa_score(y_test_big, y_pred_classes)
print("Cohen’s Kappa Score:", kappa)
results['Kappa'] = kappa
overall = pd.concat([overall, pd.DataFrame([results])], ignore_index=True)
figures.append(fig)
Model: "sequential_16"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_11 (Conv2D) (None, 128, 128, 32) 320
batch_normalization_11 (Bat (None, 128, 128, 32) 128
chNormalization)
re_lu_11 (ReLU) (None, 128, 128, 32) 0
conv2d_12 (Conv2D) (None, 128, 128, 64) 18496
batch_normalization_12 (Bat (None, 128, 128, 64) 256
chNormalization)
re_lu_12 (ReLU) (None, 128, 128, 64) 0
conv2d_13 (Conv2D) (None, 128, 128, 128) 73856
batch_normalization_13 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_13 (ReLU) (None, 128, 128, 128) 0
conv2d_14 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_14 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_14 (ReLU) (None, 128, 128, 128) 0
conv2d_15 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_15 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_15 (ReLU) (None, 128, 128, 128) 0
conv2d_16 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_16 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_16 (ReLU) (None, 128, 128, 128) 0
global_average_pooling2d_1 (None, 128) 0
(GlobalAveragePooling2D)
dense_1 (Dense) (None, 256) 33024
dense_2 (Dense) (None, 15) 3855
=================================================================
Total params: 574,735
Trainable params: 573,519
Non-trainable params: 1,216
_________________________________________________________________
Model: "CNN_Baseline"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_17 (Conv2D) (None, 128, 128, 64) 640
batch_normalization_17 (Bat (None, 128, 128, 64) 256
chNormalization)
max_pooling2d_4 (MaxPooling (None, 64, 64, 64) 0
2D)
dropout_1 (Dropout) (None, 64, 64, 64) 0
conv2d_18 (Conv2D) (None, 64, 64, 128) 73856
batch_normalization_18 (Bat (None, 64, 64, 128) 512
chNormalization)
max_pooling2d_5 (MaxPooling (None, 32, 32, 128) 0
2D)
dropout_2 (Dropout) (None, 32, 32, 128) 0
conv2d_19 (Conv2D) (None, 32, 32, 256) 295168
batch_normalization_19 (Bat (None, 32, 32, 256) 1024
chNormalization)
max_pooling2d_6 (MaxPooling (None, 16, 16, 256) 0
2D)
dropout_3 (Dropout) (None, 16, 16, 256) 0
flatten (Flatten) (None, 65536) 0
dense_3 (Dense) (None, 512) 33554944
batch_normalization_20 (Bat (None, 512) 2048
chNormalization)
dropout_4 (Dropout) (None, 512) 0
dense_4 (Dense) (None, 128) 65664
batch_normalization_21 (Bat (None, 128) 512
chNormalization)
dropout_5 (Dropout) (None, 128) 0
dense_5 (Dense) (None, 15) 1935
=================================================================
Total params: 33,996,559
Trainable params: 33,994,383
Non-trainable params: 2,176
_________________________________________________________________
Running CNN
Epoch 1/100
71/71 [==============================] - 7s 79ms/step - loss: 2.7543 - accuracy: 0.2368 - val_loss: 5.0730 - val_accuracy: 0.1283 - lr: 0.0010
Epoch 2/100
71/71 [==============================] - 5s 74ms/step - loss: 1.8582 - accuracy: 0.4283 - val_loss: 2.3013 - val_accuracy: 0.2577 - lr: 0.0010
Epoch 3/100
71/71 [==============================] - 6s 77ms/step - loss: 1.4301 - accuracy: 0.5392 - val_loss: 2.6641 - val_accuracy: 0.2480 - lr: 0.0010
Epoch 4/100
71/71 [==============================] - 5s 72ms/step - loss: 1.1399 - accuracy: 0.6303 - val_loss: 2.8095 - val_accuracy: 0.2590 - lr: 0.0010
Epoch 5/100
71/71 [==============================] - 5s 75ms/step - loss: 0.8962 - accuracy: 0.7122 - val_loss: 4.5725 - val_accuracy: 0.2420 - lr: 0.0010
Epoch 6/100
71/71 [==============================] - 5s 74ms/step - loss: 0.7132 - accuracy: 0.7697 - val_loss: 3.2076 - val_accuracy: 0.2963 - lr: 0.0010
Epoch 7/100
71/71 [==============================] - 6s 77ms/step - loss: 0.5349 - accuracy: 0.8286 - val_loss: 4.6403 - val_accuracy: 0.2663 - lr: 0.0010
Epoch 8/100
71/71 [==============================] - 5s 76ms/step - loss: 0.3972 - accuracy: 0.8824 - val_loss: 0.8181 - val_accuracy: 0.7290 - lr: 1.0000e-04
Epoch 9/100
71/71 [==============================] - 5s 76ms/step - loss: 0.3435 - accuracy: 0.8981 - val_loss: 0.7740 - val_accuracy: 0.7383 - lr: 1.0000e-04
Epoch 10/100
71/71 [==============================] - 5s 75ms/step - loss: 0.3220 - accuracy: 0.9049 - val_loss: 0.7578 - val_accuracy: 0.7420 - lr: 1.0000e-04
Epoch 11/100
71/71 [==============================] - 5s 75ms/step - loss: 0.2926 - accuracy: 0.9191 - val_loss: 0.7743 - val_accuracy: 0.7450 - lr: 1.0000e-04
Epoch 12/100
71/71 [==============================] - 6s 78ms/step - loss: 0.2719 - accuracy: 0.9248 - val_loss: 0.7849 - val_accuracy: 0.7500 - lr: 1.0000e-04
Epoch 13/100
71/71 [==============================] - 5s 76ms/step - loss: 0.2413 - accuracy: 0.9381 - val_loss: 1.0420 - val_accuracy: 0.6857 - lr: 1.0000e-04
Epoch 14/100
71/71 [==============================] - 5s 73ms/step - loss: 0.2283 - accuracy: 0.9413 - val_loss: 0.7862 - val_accuracy: 0.7583 - lr: 1.0000e-04
Epoch 15/100
71/71 [==============================] - 5s 74ms/step - loss: 0.2143 - accuracy: 0.9466 - val_loss: 0.8091 - val_accuracy: 0.7430 - lr: 1.0000e-04
Epoch 16/100
71/71 [==============================] - 5s 75ms/step - loss: 0.2012 - accuracy: 0.9483 - val_loss: 0.9459 - val_accuracy: 0.7093 - lr: 1.0000e-05
Epoch 17/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1884 - accuracy: 0.9527 - val_loss: 0.9369 - val_accuracy: 0.7147 - lr: 1.0000e-05
Epoch 18/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1910 - accuracy: 0.9535 - val_loss: 1.0690 - val_accuracy: 0.6863 - lr: 1.0000e-05
Epoch 19/100
71/71 [==============================] - 5s 73ms/step - loss: 0.1889 - accuracy: 0.9548 - val_loss: 0.9572 - val_accuracy: 0.7107 - lr: 1.0000e-05
Epoch 20/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1843 - accuracy: 0.9538 - val_loss: 0.8920 - val_accuracy: 0.7297 - lr: 1.0000e-05
Epoch 21/100
71/71 [==============================] - 5s 70ms/step - loss: 0.1862 - accuracy: 0.9505 - val_loss: 0.9494 - val_accuracy: 0.7143 - lr: 1.0000e-06
Epoch 22/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1880 - accuracy: 0.9540 - val_loss: 0.9479 - val_accuracy: 0.7147 - lr: 1.0000e-06
Epoch 23/100
71/71 [==============================] - 5s 73ms/step - loss: 0.1897 - accuracy: 0.9543 - val_loss: 0.9380 - val_accuracy: 0.7177 - lr: 1.0000e-06
Epoch 24/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1814 - accuracy: 0.9558 - val_loss: 0.9419 - val_accuracy: 0.7177 - lr: 1.0000e-06
94/94 [==============================] - 1s 4ms/step
Cohen’s Kappa Score: 0.7457142857142858
Running CNN2
Epoch 1/100
71/71 [==============================] - 19s 257ms/step - loss: 1.7964 - accuracy: 0.4401 - val_loss: 6.0117 - val_accuracy: 0.0657 - lr: 0.0010
Epoch 2/100
71/71 [==============================] - 18s 254ms/step - loss: 1.1835 - accuracy: 0.6338 - val_loss: 5.6493 - val_accuracy: 0.1140 - lr: 0.0010
Epoch 3/100
71/71 [==============================] - 18s 254ms/step - loss: 0.8984 - accuracy: 0.7221 - val_loss: 11.3253 - val_accuracy: 0.1260 - lr: 0.0010
Epoch 4/100
71/71 [==============================] - 18s 254ms/step - loss: 0.7184 - accuracy: 0.7778 - val_loss: 2.5909 - val_accuracy: 0.3733 - lr: 0.0010
Epoch 5/100
71/71 [==============================] - 18s 255ms/step - loss: 0.5847 - accuracy: 0.8226 - val_loss: 4.5517 - val_accuracy: 0.3460 - lr: 0.0010
Epoch 6/100
71/71 [==============================] - 18s 255ms/step - loss: 0.5094 - accuracy: 0.8456 - val_loss: 5.2987 - val_accuracy: 0.3873 - lr: 0.0010
Epoch 7/100
71/71 [==============================] - 18s 255ms/step - loss: 0.4177 - accuracy: 0.8739 - val_loss: 4.0588 - val_accuracy: 0.4057 - lr: 0.0010
Epoch 8/100
71/71 [==============================] - 18s 255ms/step - loss: 0.3448 - accuracy: 0.8959 - val_loss: 4.6854 - val_accuracy: 0.3503 - lr: 0.0010
Epoch 9/100
71/71 [==============================] - 18s 255ms/step - loss: 0.3041 - accuracy: 0.9089 - val_loss: 11.9348 - val_accuracy: 0.1960 - lr: 0.0010
Epoch 10/100
71/71 [==============================] - 18s 255ms/step - loss: 0.2260 - accuracy: 0.9434 - val_loss: 0.8644 - val_accuracy: 0.7350 - lr: 1.0000e-04
Epoch 11/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1910 - accuracy: 0.9539 - val_loss: 0.3603 - val_accuracy: 0.8903 - lr: 1.0000e-04
Epoch 12/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1792 - accuracy: 0.9571 - val_loss: 0.3042 - val_accuracy: 0.9040 - lr: 1.0000e-04
Epoch 13/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1702 - accuracy: 0.9611 - val_loss: 0.2840 - val_accuracy: 0.9110 - lr: 1.0000e-04
Epoch 14/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1626 - accuracy: 0.9632 - val_loss: 0.2680 - val_accuracy: 0.9177 - lr: 1.0000e-04
Epoch 15/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1559 - accuracy: 0.9653 - val_loss: 0.2570 - val_accuracy: 0.9253 - lr: 1.0000e-04
Epoch 16/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1497 - accuracy: 0.9663 - val_loss: 0.2494 - val_accuracy: 0.9297 - lr: 1.0000e-04
Epoch 17/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1439 - accuracy: 0.9685 - val_loss: 0.2360 - val_accuracy: 0.9363 - lr: 1.0000e-04
Epoch 18/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1385 - accuracy: 0.9701 - val_loss: 0.2277 - val_accuracy: 0.9390 - lr: 1.0000e-04
Epoch 19/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1333 - accuracy: 0.9720 - val_loss: 0.2222 - val_accuracy: 0.9417 - lr: 1.0000e-04
Epoch 20/100
71/71 [==============================] - 18s 256ms/step - loss: 0.1284 - accuracy: 0.9731 - val_loss: 0.2147 - val_accuracy: 0.9440 - lr: 1.0000e-04
Epoch 21/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1237 - accuracy: 0.9746 - val_loss: 0.2126 - val_accuracy: 0.9430 - lr: 1.0000e-04
Epoch 22/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1191 - accuracy: 0.9760 - val_loss: 0.2055 - val_accuracy: 0.9447 - lr: 1.0000e-04
Epoch 23/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1147 - accuracy: 0.9772 - val_loss: 0.2003 - val_accuracy: 0.9463 - lr: 1.0000e-04
Epoch 24/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1105 - accuracy: 0.9781 - val_loss: 0.2009 - val_accuracy: 0.9460 - lr: 1.0000e-04
Epoch 25/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1065 - accuracy: 0.9794 - val_loss: 0.2008 - val_accuracy: 0.9450 - lr: 1.0000e-04
Epoch 26/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1026 - accuracy: 0.9804 - val_loss: 0.2002 - val_accuracy: 0.9443 - lr: 1.0000e-04
Epoch 27/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0989 - accuracy: 0.9808 - val_loss: 0.1943 - val_accuracy: 0.9460 - lr: 1.0000e-04
Epoch 28/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0952 - accuracy: 0.9819 - val_loss: 0.1970 - val_accuracy: 0.9440 - lr: 1.0000e-04
Epoch 29/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0919 - accuracy: 0.9825 - val_loss: 0.1914 - val_accuracy: 0.9467 - lr: 1.0000e-04
Epoch 30/100
71/71 [==============================] - 18s 256ms/step - loss: 0.0884 - accuracy: 0.9834 - val_loss: 0.1802 - val_accuracy: 0.9513 - lr: 1.0000e-04
Epoch 31/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0852 - accuracy: 0.9846 - val_loss: 0.1695 - val_accuracy: 0.9543 - lr: 1.0000e-04
Epoch 32/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0820 - accuracy: 0.9856 - val_loss: 0.1675 - val_accuracy: 0.9543 - lr: 1.0000e-04
Epoch 33/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0790 - accuracy: 0.9865 - val_loss: 0.1653 - val_accuracy: 0.9540 - lr: 1.0000e-04
Epoch 34/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0762 - accuracy: 0.9878 - val_loss: 0.1624 - val_accuracy: 0.9567 - lr: 1.0000e-04
Epoch 35/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0735 - accuracy: 0.9884 - val_loss: 0.1689 - val_accuracy: 0.9533 - lr: 1.0000e-04
Epoch 36/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0708 - accuracy: 0.9890 - val_loss: 0.1613 - val_accuracy: 0.9553 - lr: 1.0000e-04
Epoch 37/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0682 - accuracy: 0.9899 - val_loss: 0.1687 - val_accuracy: 0.9513 - lr: 1.0000e-04
Epoch 38/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0656 - accuracy: 0.9907 - val_loss: 0.1536 - val_accuracy: 0.9570 - lr: 1.0000e-04
Epoch 39/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0633 - accuracy: 0.9916 - val_loss: 0.1686 - val_accuracy: 0.9517 - lr: 1.0000e-04
Epoch 40/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0608 - accuracy: 0.9924 - val_loss: 0.1594 - val_accuracy: 0.9553 - lr: 1.0000e-04
Epoch 41/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0587 - accuracy: 0.9930 - val_loss: 0.1732 - val_accuracy: 0.9490 - lr: 1.0000e-04
Epoch 42/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0564 - accuracy: 0.9937 - val_loss: 0.1567 - val_accuracy: 0.9560 - lr: 1.0000e-04
Epoch 43/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0544 - accuracy: 0.9940 - val_loss: 0.1717 - val_accuracy: 0.9507 - lr: 1.0000e-04
Epoch 44/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0490 - accuracy: 0.9937 - val_loss: 0.0988 - val_accuracy: 0.9753 - lr: 1.0000e-05
Epoch 45/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0457 - accuracy: 0.9947 - val_loss: 0.0976 - val_accuracy: 0.9757 - lr: 1.0000e-05
Epoch 46/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0449 - accuracy: 0.9953 - val_loss: 0.0964 - val_accuracy: 0.9753 - lr: 1.0000e-05
Epoch 47/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0443 - accuracy: 0.9957 - val_loss: 0.0953 - val_accuracy: 0.9767 - lr: 1.0000e-05
Epoch 48/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0439 - accuracy: 0.9957 - val_loss: 0.0949 - val_accuracy: 0.9770 - lr: 1.0000e-05
Epoch 49/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0436 - accuracy: 0.9956 - val_loss: 0.0944 - val_accuracy: 0.9773 - lr: 1.0000e-05
Epoch 50/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0433 - accuracy: 0.9958 - val_loss: 0.0939 - val_accuracy: 0.9773 - lr: 1.0000e-05
Epoch 51/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0430 - accuracy: 0.9958 - val_loss: 0.0938 - val_accuracy: 0.9773 - lr: 1.0000e-05
Epoch 52/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0427 - accuracy: 0.9959 - val_loss: 0.0935 - val_accuracy: 0.9777 - lr: 1.0000e-05
Epoch 53/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0424 - accuracy: 0.9961 - val_loss: 0.0932 - val_accuracy: 0.9777 - lr: 1.0000e-05
Epoch 54/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0421 - accuracy: 0.9962 - val_loss: 0.0929 - val_accuracy: 0.9777 - lr: 1.0000e-05
Epoch 55/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0419 - accuracy: 0.9965 - val_loss: 0.0926 - val_accuracy: 0.9777 - lr: 1.0000e-05
Epoch 56/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0416 - accuracy: 0.9966 - val_loss: 0.0923 - val_accuracy: 0.9777 - lr: 1.0000e-05
Epoch 57/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0413 - accuracy: 0.9967 - val_loss: 0.0920 - val_accuracy: 0.9773 - lr: 1.0000e-05
Epoch 58/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0411 - accuracy: 0.9968 - val_loss: 0.0918 - val_accuracy: 0.9773 - lr: 1.0000e-05
Epoch 59/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0408 - accuracy: 0.9968 - val_loss: 0.0916 - val_accuracy: 0.9773 - lr: 1.0000e-05
Epoch 60/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0406 - accuracy: 0.9969 - val_loss: 0.0914 - val_accuracy: 0.9773 - lr: 1.0000e-05
Epoch 61/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0403 - accuracy: 0.9969 - val_loss: 0.0911 - val_accuracy: 0.9777 - lr: 1.0000e-05
Epoch 62/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0400 - accuracy: 0.9969 - val_loss: 0.0909 - val_accuracy: 0.9780 - lr: 1.0000e-05
Epoch 63/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0398 - accuracy: 0.9970 - val_loss: 0.0907 - val_accuracy: 0.9780 - lr: 1.0000e-05
Epoch 64/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0395 - accuracy: 0.9970 - val_loss: 0.0905 - val_accuracy: 0.9780 - lr: 1.0000e-05
Epoch 65/100
71/71 [==============================] - 18s 256ms/step - loss: 0.0393 - accuracy: 0.9970 - val_loss: 0.0903 - val_accuracy: 0.9787 - lr: 1.0000e-05
Epoch 66/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0390 - accuracy: 0.9970 - val_loss: 0.0901 - val_accuracy: 0.9787 - lr: 1.0000e-05
Epoch 67/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0388 - accuracy: 0.9970 - val_loss: 0.0899 - val_accuracy: 0.9790 - lr: 1.0000e-05
Epoch 68/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0385 - accuracy: 0.9970 - val_loss: 0.0897 - val_accuracy: 0.9787 - lr: 1.0000e-05
Epoch 69/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0383 - accuracy: 0.9972 - val_loss: 0.0894 - val_accuracy: 0.9783 - lr: 1.0000e-05
Epoch 70/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0381 - accuracy: 0.9972 - val_loss: 0.0891 - val_accuracy: 0.9790 - lr: 1.0000e-05
Epoch 71/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0378 - accuracy: 0.9973 - val_loss: 0.0892 - val_accuracy: 0.9783 - lr: 1.0000e-05
Epoch 72/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0376 - accuracy: 0.9975 - val_loss: 0.0888 - val_accuracy: 0.9790 - lr: 1.0000e-05
Epoch 73/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0373 - accuracy: 0.9975 - val_loss: 0.0886 - val_accuracy: 0.9787 - lr: 1.0000e-05
Epoch 74/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0371 - accuracy: 0.9975 - val_loss: 0.0884 - val_accuracy: 0.9787 - lr: 1.0000e-05
Epoch 75/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0369 - accuracy: 0.9976 - val_loss: 0.0883 - val_accuracy: 0.9790 - lr: 1.0000e-05
Epoch 76/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0366 - accuracy: 0.9977 - val_loss: 0.0880 - val_accuracy: 0.9787 - lr: 1.0000e-05
Epoch 77/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0364 - accuracy: 0.9977 - val_loss: 0.0878 - val_accuracy: 0.9787 - lr: 1.0000e-05
94/94 [==============================] - 2s 16ms/step
Cohen’s Kappa Score: 0.9792857142857143
Running VGG_Baseline
Epoch 1/100
71/71 [==============================] - 8s 81ms/step - loss: 3.7130 - accuracy: 0.3398 - val_loss: 194.5654 - val_accuracy: 0.0943 - lr: 0.0100
Epoch 2/100
71/71 [==============================] - 5s 74ms/step - loss: 2.2185 - accuracy: 0.4681 - val_loss: 4.6053 - val_accuracy: 0.1047 - lr: 0.0100
Epoch 3/100
71/71 [==============================] - 5s 76ms/step - loss: 1.9828 - accuracy: 0.5214 - val_loss: 10.5360 - val_accuracy: 0.0900 - lr: 0.0100
Epoch 4/100
71/71 [==============================] - 5s 76ms/step - loss: 1.8593 - accuracy: 0.5576 - val_loss: 22.4761 - val_accuracy: 0.0703 - lr: 0.0100
Epoch 5/100
71/71 [==============================] - 5s 75ms/step - loss: 1.7820 - accuracy: 0.5923 - val_loss: 3.6513 - val_accuracy: 0.1763 - lr: 0.0100
Epoch 6/100
71/71 [==============================] - 5s 75ms/step - loss: 1.7037 - accuracy: 0.6108 - val_loss: 4.1829 - val_accuracy: 0.1913 - lr: 0.0100
Epoch 7/100
71/71 [==============================] - 5s 73ms/step - loss: 1.7713 - accuracy: 0.5986 - val_loss: 3.9473 - val_accuracy: 0.2930 - lr: 0.0100
Epoch 8/100
71/71 [==============================] - 5s 76ms/step - loss: 1.6542 - accuracy: 0.6253 - val_loss: 3.2740 - val_accuracy: 0.2683 - lr: 0.0100
Epoch 9/100
71/71 [==============================] - 5s 74ms/step - loss: 1.5704 - accuracy: 0.6324 - val_loss: 3.8286 - val_accuracy: 0.2593 - lr: 0.0100
Epoch 10/100
71/71 [==============================] - 5s 74ms/step - loss: 1.4600 - accuracy: 0.6678 - val_loss: 3.3423 - val_accuracy: 0.1970 - lr: 0.0100
Epoch 11/100
71/71 [==============================] - 5s 74ms/step - loss: 1.4761 - accuracy: 0.6649 - val_loss: 6.4964 - val_accuracy: 0.1470 - lr: 0.0100
Epoch 12/100
71/71 [==============================] - 5s 75ms/step - loss: 1.3569 - accuracy: 0.6890 - val_loss: 2.6734 - val_accuracy: 0.3307 - lr: 0.0100
Epoch 13/100
71/71 [==============================] - 5s 76ms/step - loss: 1.3813 - accuracy: 0.6940 - val_loss: 2.7017 - val_accuracy: 0.3423 - lr: 0.0100
Epoch 14/100
71/71 [==============================] - 5s 75ms/step - loss: 1.3036 - accuracy: 0.7157 - val_loss: 3.1741 - val_accuracy: 0.2693 - lr: 0.0100
Epoch 15/100
71/71 [==============================] - 5s 77ms/step - loss: 1.2600 - accuracy: 0.7199 - val_loss: 7.1163 - val_accuracy: 0.2060 - lr: 0.0100
Epoch 16/100
71/71 [==============================] - 5s 75ms/step - loss: 1.2386 - accuracy: 0.7373 - val_loss: 2.6765 - val_accuracy: 0.4473 - lr: 0.0100
Epoch 17/100
71/71 [==============================] - 5s 76ms/step - loss: 1.1756 - accuracy: 0.7475 - val_loss: 4.8358 - val_accuracy: 0.1360 - lr: 0.0100
Epoch 18/100
71/71 [==============================] - 6s 78ms/step - loss: 0.9255 - accuracy: 0.8226 - val_loss: 1.8162 - val_accuracy: 0.5363 - lr: 1.0000e-03
Epoch 19/100
71/71 [==============================] - 6s 78ms/step - loss: 0.7819 - accuracy: 0.8586 - val_loss: 1.8051 - val_accuracy: 0.5377 - lr: 1.0000e-03
Epoch 20/100
71/71 [==============================] - 5s 77ms/step - loss: 0.7028 - accuracy: 0.8743 - val_loss: 1.3342 - val_accuracy: 0.6633 - lr: 1.0000e-03
Epoch 21/100
71/71 [==============================] - 5s 75ms/step - loss: 0.6385 - accuracy: 0.8886 - val_loss: 1.2191 - val_accuracy: 0.7057 - lr: 1.0000e-03
Epoch 22/100
71/71 [==============================] - 6s 77ms/step - loss: 0.5833 - accuracy: 0.8992 - val_loss: 1.1689 - val_accuracy: 0.7313 - lr: 1.0000e-03
Epoch 23/100
71/71 [==============================] - 5s 74ms/step - loss: 0.5474 - accuracy: 0.9055 - val_loss: 0.9183 - val_accuracy: 0.7980 - lr: 1.0000e-03
Epoch 24/100
71/71 [==============================] - 5s 76ms/step - loss: 0.5104 - accuracy: 0.9147 - val_loss: 0.7994 - val_accuracy: 0.8360 - lr: 1.0000e-03
Epoch 25/100
71/71 [==============================] - 5s 74ms/step - loss: 0.4744 - accuracy: 0.9221 - val_loss: 0.8342 - val_accuracy: 0.8097 - lr: 1.0000e-03
Epoch 26/100
71/71 [==============================] - 5s 76ms/step - loss: 0.4518 - accuracy: 0.9266 - val_loss: 0.6993 - val_accuracy: 0.8503 - lr: 1.0000e-03
Epoch 27/100
71/71 [==============================] - 5s 74ms/step - loss: 0.4277 - accuracy: 0.9334 - val_loss: 0.6207 - val_accuracy: 0.8803 - lr: 1.0000e-03
Epoch 28/100
71/71 [==============================] - 5s 74ms/step - loss: 0.4027 - accuracy: 0.9421 - val_loss: 0.7179 - val_accuracy: 0.8340 - lr: 1.0000e-03
Epoch 29/100
71/71 [==============================] - 5s 76ms/step - loss: 0.4019 - accuracy: 0.9368 - val_loss: 0.7397 - val_accuracy: 0.8200 - lr: 1.0000e-03
Epoch 30/100
71/71 [==============================] - 5s 75ms/step - loss: 0.3659 - accuracy: 0.9478 - val_loss: 0.7312 - val_accuracy: 0.8233 - lr: 1.0000e-03
Epoch 31/100
71/71 [==============================] - 5s 75ms/step - loss: 0.3441 - accuracy: 0.9526 - val_loss: 0.6943 - val_accuracy: 0.8330 - lr: 1.0000e-03
Epoch 32/100
71/71 [==============================] - 5s 73ms/step - loss: 0.3498 - accuracy: 0.9489 - val_loss: 0.7788 - val_accuracy: 0.8100 - lr: 1.0000e-03
Epoch 33/100
71/71 [==============================] - 5s 74ms/step - loss: 0.2989 - accuracy: 0.9673 - val_loss: 0.4253 - val_accuracy: 0.9267 - lr: 1.0000e-04
Epoch 34/100
71/71 [==============================] - 5s 76ms/step - loss: 0.2694 - accuracy: 0.9781 - val_loss: 0.3433 - val_accuracy: 0.9530 - lr: 1.0000e-04
Epoch 35/100
71/71 [==============================] - 5s 77ms/step - loss: 0.2608 - accuracy: 0.9806 - val_loss: 0.3283 - val_accuracy: 0.9587 - lr: 1.0000e-04
Epoch 36/100
71/71 [==============================] - 5s 75ms/step - loss: 0.2576 - accuracy: 0.9816 - val_loss: 0.3252 - val_accuracy: 0.9600 - lr: 1.0000e-04
Epoch 37/100
71/71 [==============================] - 5s 74ms/step - loss: 0.2502 - accuracy: 0.9845 - val_loss: 0.3223 - val_accuracy: 0.9600 - lr: 1.0000e-04
Epoch 38/100
71/71 [==============================] - 5s 75ms/step - loss: 0.2461 - accuracy: 0.9845 - val_loss: 0.3159 - val_accuracy: 0.9630 - lr: 1.0000e-04
Epoch 39/100
71/71 [==============================] - 5s 76ms/step - loss: 0.2439 - accuracy: 0.9834 - val_loss: 0.3078 - val_accuracy: 0.9630 - lr: 1.0000e-04
Epoch 40/100
71/71 [==============================] - 5s 76ms/step - loss: 0.2371 - accuracy: 0.9865 - val_loss: 0.3088 - val_accuracy: 0.9633 - lr: 1.0000e-04
Epoch 41/100
71/71 [==============================] - 5s 77ms/step - loss: 0.2338 - accuracy: 0.9863 - val_loss: 0.3034 - val_accuracy: 0.9653 - lr: 1.0000e-04
Epoch 42/100
71/71 [==============================] - 5s 75ms/step - loss: 0.2303 - accuracy: 0.9884 - val_loss: 0.3031 - val_accuracy: 0.9633 - lr: 1.0000e-04
Epoch 43/100
71/71 [==============================] - 6s 78ms/step - loss: 0.2288 - accuracy: 0.9878 - val_loss: 0.2998 - val_accuracy: 0.9640 - lr: 1.0000e-04
Epoch 44/100
71/71 [==============================] - 5s 76ms/step - loss: 0.2261 - accuracy: 0.9884 - val_loss: 0.2980 - val_accuracy: 0.9653 - lr: 1.0000e-04
Epoch 45/100
71/71 [==============================] - 5s 74ms/step - loss: 0.2204 - accuracy: 0.9909 - val_loss: 0.2892 - val_accuracy: 0.9667 - lr: 1.0000e-04
Epoch 46/100
71/71 [==============================] - 5s 73ms/step - loss: 0.2182 - accuracy: 0.9904 - val_loss: 0.2898 - val_accuracy: 0.9653 - lr: 1.0000e-04
Epoch 47/100
71/71 [==============================] - 5s 75ms/step - loss: 0.2195 - accuracy: 0.9879 - val_loss: 0.2866 - val_accuracy: 0.9657 - lr: 1.0000e-04
Epoch 48/100
71/71 [==============================] - 5s 73ms/step - loss: 0.2139 - accuracy: 0.9907 - val_loss: 0.2889 - val_accuracy: 0.9643 - lr: 1.0000e-04
Epoch 49/100
71/71 [==============================] - 5s 75ms/step - loss: 0.2108 - accuracy: 0.9915 - val_loss: 0.2854 - val_accuracy: 0.9653 - lr: 1.0000e-04
Epoch 50/100
71/71 [==============================] - 5s 76ms/step - loss: 0.2077 - accuracy: 0.9918 - val_loss: 0.2826 - val_accuracy: 0.9643 - lr: 1.0000e-04
Epoch 51/100
71/71 [==============================] - 5s 76ms/step - loss: 0.2054 - accuracy: 0.9918 - val_loss: 0.2812 - val_accuracy: 0.9657 - lr: 1.0000e-04
Epoch 52/100
71/71 [==============================] - 5s 75ms/step - loss: 0.2013 - accuracy: 0.9930 - val_loss: 0.2790 - val_accuracy: 0.9673 - lr: 1.0000e-04
Epoch 53/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1994 - accuracy: 0.9937 - val_loss: 0.2765 - val_accuracy: 0.9657 - lr: 1.0000e-04
Epoch 54/100
71/71 [==============================] - 6s 78ms/step - loss: 0.1991 - accuracy: 0.9932 - val_loss: 0.2702 - val_accuracy: 0.9687 - lr: 1.0000e-04
Epoch 55/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1954 - accuracy: 0.9947 - val_loss: 0.2685 - val_accuracy: 0.9683 - lr: 1.0000e-04
Epoch 56/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1917 - accuracy: 0.9945 - val_loss: 0.2659 - val_accuracy: 0.9700 - lr: 1.0000e-04
Epoch 57/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1887 - accuracy: 0.9960 - val_loss: 0.2707 - val_accuracy: 0.9667 - lr: 1.0000e-04
Epoch 58/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1864 - accuracy: 0.9952 - val_loss: 0.2661 - val_accuracy: 0.9677 - lr: 1.0000e-04
Epoch 59/100
71/71 [==============================] - 6s 77ms/step - loss: 0.1862 - accuracy: 0.9952 - val_loss: 0.2745 - val_accuracy: 0.9660 - lr: 1.0000e-04
Epoch 60/100
71/71 [==============================] - 5s 73ms/step - loss: 0.1829 - accuracy: 0.9962 - val_loss: 0.2648 - val_accuracy: 0.9693 - lr: 1.0000e-04
Epoch 61/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1799 - accuracy: 0.9966 - val_loss: 0.2597 - val_accuracy: 0.9687 - lr: 1.0000e-04
Epoch 62/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1778 - accuracy: 0.9966 - val_loss: 0.2646 - val_accuracy: 0.9650 - lr: 1.0000e-04
Epoch 63/100
71/71 [==============================] - 5s 73ms/step - loss: 0.1785 - accuracy: 0.9956 - val_loss: 0.2587 - val_accuracy: 0.9693 - lr: 1.0000e-04
Epoch 64/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1738 - accuracy: 0.9971 - val_loss: 0.2594 - val_accuracy: 0.9683 - lr: 1.0000e-04
Epoch 65/100
71/71 [==============================] - 6s 77ms/step - loss: 0.1714 - accuracy: 0.9975 - val_loss: 0.2574 - val_accuracy: 0.9710 - lr: 1.0000e-04
Epoch 66/100
71/71 [==============================] - 5s 73ms/step - loss: 0.1702 - accuracy: 0.9969 - val_loss: 0.2644 - val_accuracy: 0.9680 - lr: 1.0000e-04
Epoch 67/100
71/71 [==============================] - 5s 77ms/step - loss: 0.1675 - accuracy: 0.9979 - val_loss: 0.2539 - val_accuracy: 0.9673 - lr: 1.0000e-04
Epoch 68/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1661 - accuracy: 0.9979 - val_loss: 0.2544 - val_accuracy: 0.9677 - lr: 1.0000e-04
Epoch 69/100
71/71 [==============================] - 6s 77ms/step - loss: 0.1638 - accuracy: 0.9979 - val_loss: 0.2470 - val_accuracy: 0.9697 - lr: 1.0000e-04
Epoch 70/100
71/71 [==============================] - 5s 77ms/step - loss: 0.1623 - accuracy: 0.9978 - val_loss: 0.2524 - val_accuracy: 0.9710 - lr: 1.0000e-04
Epoch 71/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1598 - accuracy: 0.9990 - val_loss: 0.2436 - val_accuracy: 0.9713 - lr: 1.0000e-04
Epoch 72/100
71/71 [==============================] - 5s 77ms/step - loss: 0.1595 - accuracy: 0.9983 - val_loss: 0.2326 - val_accuracy: 0.9720 - lr: 1.0000e-04
Epoch 73/100
71/71 [==============================] - 5s 77ms/step - loss: 0.1572 - accuracy: 0.9988 - val_loss: 0.2475 - val_accuracy: 0.9700 - lr: 1.0000e-04
Epoch 74/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1592 - accuracy: 0.9972 - val_loss: 0.2484 - val_accuracy: 0.9710 - lr: 1.0000e-04
Epoch 75/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1551 - accuracy: 0.9984 - val_loss: 0.2431 - val_accuracy: 0.9717 - lr: 1.0000e-04
Epoch 76/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1518 - accuracy: 0.9986 - val_loss: 0.2388 - val_accuracy: 0.9713 - lr: 1.0000e-04
Epoch 77/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1523 - accuracy: 0.9982 - val_loss: 0.2381 - val_accuracy: 0.9723 - lr: 1.0000e-04
Epoch 78/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1483 - accuracy: 0.9991 - val_loss: 0.2286 - val_accuracy: 0.9743 - lr: 1.0000e-05
Epoch 79/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1482 - accuracy: 0.9988 - val_loss: 0.2259 - val_accuracy: 0.9753 - lr: 1.0000e-05
Epoch 80/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1474 - accuracy: 0.9994 - val_loss: 0.2251 - val_accuracy: 0.9750 - lr: 1.0000e-05
Epoch 81/100
71/71 [==============================] - 5s 74ms/step - loss: 0.1458 - accuracy: 0.9998 - val_loss: 0.2241 - val_accuracy: 0.9750 - lr: 1.0000e-05
Epoch 82/100
71/71 [==============================] - 5s 77ms/step - loss: 0.1465 - accuracy: 0.9991 - val_loss: 0.2249 - val_accuracy: 0.9743 - lr: 1.0000e-05
Epoch 83/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1465 - accuracy: 0.9992 - val_loss: 0.2227 - val_accuracy: 0.9753 - lr: 1.0000e-05
Epoch 84/100
71/71 [==============================] - 6s 78ms/step - loss: 0.1454 - accuracy: 0.9994 - val_loss: 0.2222 - val_accuracy: 0.9743 - lr: 1.0000e-05
Epoch 85/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1453 - accuracy: 0.9994 - val_loss: 0.2246 - val_accuracy: 0.9747 - lr: 1.0000e-05
Epoch 86/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1446 - accuracy: 0.9996 - val_loss: 0.2227 - val_accuracy: 0.9753 - lr: 1.0000e-05
Epoch 87/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1455 - accuracy: 0.9996 - val_loss: 0.2218 - val_accuracy: 0.9760 - lr: 1.0000e-05
Epoch 88/100
71/71 [==============================] - 6s 78ms/step - loss: 0.1441 - accuracy: 0.9998 - val_loss: 0.2208 - val_accuracy: 0.9763 - lr: 1.0000e-05
Epoch 89/100
71/71 [==============================] - 5s 72ms/step - loss: 0.1451 - accuracy: 0.9994 - val_loss: 0.2222 - val_accuracy: 0.9753 - lr: 1.0000e-05
Epoch 90/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1445 - accuracy: 0.9993 - val_loss: 0.2208 - val_accuracy: 0.9757 - lr: 1.0000e-05
Epoch 91/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1444 - accuracy: 0.9994 - val_loss: 0.2206 - val_accuracy: 0.9760 - lr: 1.0000e-05
Epoch 92/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1428 - accuracy: 0.9999 - val_loss: 0.2210 - val_accuracy: 0.9760 - lr: 1.0000e-05
Epoch 93/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1432 - accuracy: 0.9999 - val_loss: 0.2221 - val_accuracy: 0.9750 - lr: 1.0000e-05
Epoch 94/100
71/71 [==============================] - 5s 77ms/step - loss: 0.1428 - accuracy: 0.9999 - val_loss: 0.2221 - val_accuracy: 0.9753 - lr: 1.0000e-05
Epoch 95/100
71/71 [==============================] - 6s 78ms/step - loss: 0.1427 - accuracy: 0.9998 - val_loss: 0.2234 - val_accuracy: 0.9753 - lr: 1.0000e-05
Epoch 96/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1433 - accuracy: 0.9991 - val_loss: 0.2223 - val_accuracy: 0.9757 - lr: 1.0000e-05
Epoch 97/100
71/71 [==============================] - 5s 76ms/step - loss: 0.1427 - accuracy: 0.9997 - val_loss: 0.2228 - val_accuracy: 0.9757 - lr: 1.0000e-06
Epoch 98/100
71/71 [==============================] - 5s 75ms/step - loss: 0.1427 - accuracy: 0.9994 - val_loss: 0.2229 - val_accuracy: 0.9753 - lr: 1.0000e-06
94/94 [==============================] - 1s 5ms/step
Cohen’s Kappa Score: 0.9746428571428571
for fig in figures:
fig()
overall.iloc[-3:]
| Model Name | Epochs | Batch Size | Train Loss | Test Loss | Train Acc | Test Acc | Kappa | Comments | |
|---|---|---|---|---|---|---|---|---|---|
| 3 | CNN basic two | 24 | 128 | 0.228279 | 0.786181 | 0.941294 | 0.758333 | 0.745714 | NaN |
| 4 | CNN2 basic two | 77 | 128 | 0.038791 | 0.089882 | 0.997009 | 0.979000 | 0.979286 | NaN |
| 5 | VGG_Baseline basic two | 98 | 128 | 0.144072 | 0.220837 | 0.999778 | 0.976333 | 0.974643 | NaN |
Cutmix Data Augmentation is
def preprocess_data(image, label):
image = tf.expand_dims(image, -1)
return image, label
def sample_beta_distribution(size, concentration_0=300, concentration_1=0.1):
gamma_1_sample = tf.random.gamma(shape=[size], alpha=concentration_1)
gamma_2_sample = tf.random.gamma(shape=[size], alpha=concentration_0)
return gamma_1_sample / (gamma_1_sample + gamma_2_sample)
@tf.function
def get_box(lambda_value):
cut_rat = tf.math.sqrt(1.0 - lambda_value)
image_wh = (31,31,1)[0]
cut_wh = image_wh * cut_rat # rw
cut_wh = tf.cast(cut_wh, tf.int32)
cut_x = tf.random.uniform((1,), minval=0, maxval=image_wh, dtype=tf.int32) # rx
cut_y = tf.random.uniform((1,), minval=0, maxval=image_wh, dtype=tf.int32) # ry
boundaryx1 = tf.clip_by_value(cut_x[0] - cut_wh // 2, 0, image_wh)
boundaryy1 = tf.clip_by_value(cut_y[0] - cut_wh // 2, 0, image_wh)
bbx2 = tf.clip_by_value(cut_x[0] + cut_wh // 2, 0, image_wh)
bby2 = tf.clip_by_value(cut_y[0] + cut_wh // 2, 0, image_wh)
target_h = bby2 - boundaryy1
if target_h == 0:
target_h += 1
target_w = bbx2 - boundaryx1
if target_w == 0:
target_w += 1
return boundaryx1, boundaryy1, target_h, target_w
@tf.function
def cutmix(train_ds_one, train_ds_two):
(image1, label1), (image2, label2) = train_ds_one, train_ds_two
image_size = (128,128,1)[0]
alpha = [1]
beta = [1]
lambda_value = sample_beta_distribution(1, alpha, beta)
lambda_value = lambda_value[0][0]
boundaryx1, boundaryy1, target_h, target_w = get_box(lambda_value)
crop2 = tf.image.crop_to_bounding_box(
image2, boundaryy1, boundaryx1, target_h, target_w
)
image2 = tf.image.pad_to_bounding_box(
crop2, boundaryy1, boundaryx1, image_size, image_size
)
crop1 = tf.image.crop_to_bounding_box(
image1, boundaryy1, boundaryx1, target_h, target_w
)
img1 = tf.image.pad_to_bounding_box(
crop1, boundaryy1, boundaryx1, image_size, image_size
)
image1 = image1 - img1
image = image1 + image2
lambda_value = 1 - (target_w * target_h) / (image_size * image_size)
lambda_value = tf.cast(lambda_value, tf.float32)
label = lambda_value * label1 + (1 - lambda_value) * label2
return image, label
train_ds_one = tf.data.Dataset.from_tensor_slices((X_train_big, y_train_big)).shuffle(2048).map(preprocess_data, num_parallel_calls=tf.data.AUTOTUNE)
train_ds_two = tf.data.Dataset.from_tensor_slices((X_train_big, y_train_big)).shuffle(2048).map(preprocess_data, num_parallel_calls=tf.data.AUTOTUNE)
val_ds_cutmix = tf.data.Dataset.from_tensor_slices((X_val_big, y_val_big))
val_ds_cutmix = val_ds_cutmix.map(preprocess_data, num_parallel_calls=tf.data.AUTOTUNE).batch(128).prefetch(tf.data.AUTOTUNE)
train_ds_cutmix = tf.data.Dataset.zip((train_ds_one, train_ds_two))
train_ds_cutmix = (
train_ds_cutmix.shuffle(1024)
.map(cutmix, num_parallel_calls=tf.data.AUTOTUNE)
.batch(128)
.prefetch(tf.data.AUTOTUNE)
)
train_ds_cutmix
image_batch, label_batch = next(iter(train_ds_cutmix))
plt.figure(figsize=(10, 10))
for i in range(15):
ax = plt.subplot(3, 5, i + 1)
plt.title(labels_dict[np.argmax(label_batch[i])])
plt.imshow(tf.squeeze(image_batch[i]), cmap="gray")
plt.axis("off")
plt.show()
models = models_array()
figures_cutmix = []
for i in range(len(models)):
print(f'Running {model_names[i]}')
results, fig = evaluator.model_evaluate( train_ds_cutmix, val_ds_cutmix , models[i], base_hparams)
results['Model Name'] = f'{model_names[i]} cutmix'
y_pred = models[i].predict(X_test_big)
y_pred_classes = np.argmax(y_pred, axis=1)
kappa = cohen_kappa_score(y_test_big, y_pred_classes)
print("Cohen’s Kappa Score:", kappa)
results['Kappa'] = kappa
overall = pd.concat([overall, pd.DataFrame([results])], ignore_index=True)
figures_cutmix.append(fig)
Model: "sequential_16"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_11 (Conv2D) (None, 128, 128, 32) 320
batch_normalization_11 (Bat (None, 128, 128, 32) 128
chNormalization)
re_lu_11 (ReLU) (None, 128, 128, 32) 0
conv2d_12 (Conv2D) (None, 128, 128, 64) 18496
batch_normalization_12 (Bat (None, 128, 128, 64) 256
chNormalization)
re_lu_12 (ReLU) (None, 128, 128, 64) 0
conv2d_13 (Conv2D) (None, 128, 128, 128) 73856
batch_normalization_13 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_13 (ReLU) (None, 128, 128, 128) 0
conv2d_14 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_14 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_14 (ReLU) (None, 128, 128, 128) 0
conv2d_15 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_15 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_15 (ReLU) (None, 128, 128, 128) 0
conv2d_16 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_16 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_16 (ReLU) (None, 128, 128, 128) 0
global_average_pooling2d_1 (None, 128) 0
(GlobalAveragePooling2D)
dense_1 (Dense) (None, 256) 33024
dense_2 (Dense) (None, 15) 3855
=================================================================
Total params: 574,735
Trainable params: 573,519
Non-trainable params: 1,216
_________________________________________________________________
Model: "CNN_Baseline"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_17 (Conv2D) (None, 128, 128, 64) 640
batch_normalization_17 (Bat (None, 128, 128, 64) 256
chNormalization)
max_pooling2d_4 (MaxPooling (None, 64, 64, 64) 0
2D)
dropout_1 (Dropout) (None, 64, 64, 64) 0
conv2d_18 (Conv2D) (None, 64, 64, 128) 73856
batch_normalization_18 (Bat (None, 64, 64, 128) 512
chNormalization)
max_pooling2d_5 (MaxPooling (None, 32, 32, 128) 0
2D)
dropout_2 (Dropout) (None, 32, 32, 128) 0
conv2d_19 (Conv2D) (None, 32, 32, 256) 295168
batch_normalization_19 (Bat (None, 32, 32, 256) 1024
chNormalization)
max_pooling2d_6 (MaxPooling (None, 16, 16, 256) 0
2D)
dropout_3 (Dropout) (None, 16, 16, 256) 0
flatten (Flatten) (None, 65536) 0
dense_3 (Dense) (None, 512) 33554944
batch_normalization_20 (Bat (None, 512) 2048
chNormalization)
dropout_4 (Dropout) (None, 512) 0
dense_4 (Dense) (None, 128) 65664
batch_normalization_21 (Bat (None, 128) 512
chNormalization)
dropout_5 (Dropout) (None, 128) 0
dense_5 (Dense) (None, 15) 1935
=================================================================
Total params: 33,996,559
Trainable params: 33,994,383
Non-trainable params: 2,176
_________________________________________________________________
Running CNN
Epoch 1/100
71/71 [==============================] - 5s 55ms/step - loss: 2.3340 - accuracy: 0.3566 - val_loss: 3.0086 - val_accuracy: 0.1610 - lr: 0.0010
Epoch 2/100
71/71 [==============================] - 3s 48ms/step - loss: 1.3583 - accuracy: 0.6002 - val_loss: 2.6224 - val_accuracy: 0.1763 - lr: 0.0010
Epoch 3/100
71/71 [==============================] - 3s 48ms/step - loss: 0.9679 - accuracy: 0.7311 - val_loss: 3.6664 - val_accuracy: 0.1900 - lr: 0.0010
Epoch 4/100
71/71 [==============================] - 4s 49ms/step - loss: 0.7119 - accuracy: 0.8118 - val_loss: 1.8500 - val_accuracy: 0.4633 - lr: 0.0010
Epoch 5/100
71/71 [==============================] - 4s 50ms/step - loss: 0.5490 - accuracy: 0.8738 - val_loss: 0.7664 - val_accuracy: 0.7443 - lr: 0.0010
Epoch 6/100
71/71 [==============================] - 3s 48ms/step - loss: 0.4413 - accuracy: 0.9045 - val_loss: 0.6372 - val_accuracy: 0.8043 - lr: 0.0010
Epoch 7/100
71/71 [==============================] - 3s 48ms/step - loss: 0.3908 - accuracy: 0.9256 - val_loss: 0.5686 - val_accuracy: 0.8243 - lr: 0.0010
Epoch 8/100
71/71 [==============================] - 3s 47ms/step - loss: 0.3374 - accuracy: 0.9451 - val_loss: 1.8107 - val_accuracy: 0.6000 - lr: 0.0010
Epoch 9/100
71/71 [==============================] - 3s 47ms/step - loss: 0.3108 - accuracy: 0.9548 - val_loss: 0.9040 - val_accuracy: 0.7207 - lr: 0.0010
Epoch 10/100
71/71 [==============================] - 3s 48ms/step - loss: 0.2908 - accuracy: 0.9620 - val_loss: 0.3898 - val_accuracy: 0.8763 - lr: 0.0010
Epoch 11/100
71/71 [==============================] - 3s 48ms/step - loss: 0.2621 - accuracy: 0.9728 - val_loss: 0.3589 - val_accuracy: 0.8880 - lr: 0.0010
Epoch 12/100
71/71 [==============================] - 3s 47ms/step - loss: 0.2448 - accuracy: 0.9795 - val_loss: 0.3828 - val_accuracy: 0.8843 - lr: 0.0010
Epoch 13/100
71/71 [==============================] - 3s 47ms/step - loss: 0.2302 - accuracy: 0.9839 - val_loss: 0.9990 - val_accuracy: 0.7400 - lr: 0.0010
Epoch 14/100
71/71 [==============================] - 3s 48ms/step - loss: 0.2194 - accuracy: 0.9874 - val_loss: 0.3257 - val_accuracy: 0.9053 - lr: 0.0010
Epoch 15/100
71/71 [==============================] - 3s 47ms/step - loss: 0.2139 - accuracy: 0.9883 - val_loss: 0.3066 - val_accuracy: 0.9040 - lr: 0.0010
Epoch 16/100
71/71 [==============================] - 3s 47ms/step - loss: 0.2055 - accuracy: 0.9910 - val_loss: 0.5151 - val_accuracy: 0.8453 - lr: 0.0010
Epoch 17/100
71/71 [==============================] - 3s 47ms/step - loss: 0.2040 - accuracy: 0.9907 - val_loss: 0.7521 - val_accuracy: 0.7897 - lr: 0.0010
Epoch 18/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1961 - accuracy: 0.9937 - val_loss: 0.5512 - val_accuracy: 0.8383 - lr: 0.0010
Epoch 19/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1928 - accuracy: 0.9947 - val_loss: 0.7449 - val_accuracy: 0.8017 - lr: 0.0010
Epoch 20/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1873 - accuracy: 0.9958 - val_loss: 0.2979 - val_accuracy: 0.9097 - lr: 0.0010
Epoch 21/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1863 - accuracy: 0.9951 - val_loss: 0.4013 - val_accuracy: 0.8800 - lr: 0.0010
Epoch 22/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1828 - accuracy: 0.9963 - val_loss: 0.2471 - val_accuracy: 0.9270 - lr: 0.0010
Epoch 23/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1918 - accuracy: 0.9939 - val_loss: 3.1083 - val_accuracy: 0.4700 - lr: 0.0010
Epoch 24/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1806 - accuracy: 0.9963 - val_loss: 0.3412 - val_accuracy: 0.8960 - lr: 0.0010
Epoch 25/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1772 - accuracy: 0.9971 - val_loss: 0.4389 - val_accuracy: 0.8650 - lr: 0.0010
Epoch 26/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1742 - accuracy: 0.9979 - val_loss: 0.6846 - val_accuracy: 0.8060 - lr: 0.0010
Epoch 27/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1732 - accuracy: 0.9976 - val_loss: 0.6614 - val_accuracy: 0.8153 - lr: 0.0010
Epoch 28/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1691 - accuracy: 0.9988 - val_loss: 0.2010 - val_accuracy: 0.9373 - lr: 1.0000e-04
Epoch 29/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1650 - accuracy: 0.9988 - val_loss: 0.1795 - val_accuracy: 0.9430 - lr: 1.0000e-04
Epoch 30/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1651 - accuracy: 0.9986 - val_loss: 0.2967 - val_accuracy: 0.9063 - lr: 1.0000e-04
Epoch 31/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1651 - accuracy: 0.9991 - val_loss: 0.1787 - val_accuracy: 0.9437 - lr: 1.0000e-04
Epoch 32/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1645 - accuracy: 0.9989 - val_loss: 0.1789 - val_accuracy: 0.9457 - lr: 1.0000e-04
Epoch 33/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1642 - accuracy: 0.9992 - val_loss: 0.1862 - val_accuracy: 0.9420 - lr: 1.0000e-04
Epoch 34/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1648 - accuracy: 0.9993 - val_loss: 0.1792 - val_accuracy: 0.9430 - lr: 1.0000e-04
Epoch 35/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1640 - accuracy: 0.9991 - val_loss: 0.1871 - val_accuracy: 0.9407 - lr: 1.0000e-04
Epoch 36/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1630 - accuracy: 0.9989 - val_loss: 0.2339 - val_accuracy: 0.9273 - lr: 1.0000e-04
Epoch 37/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1617 - accuracy: 0.9994 - val_loss: 0.2055 - val_accuracy: 0.9347 - lr: 1.0000e-05
Epoch 38/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1620 - accuracy: 0.9994 - val_loss: 0.1950 - val_accuracy: 0.9407 - lr: 1.0000e-05
Epoch 39/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1614 - accuracy: 0.9999 - val_loss: 0.1952 - val_accuracy: 0.9413 - lr: 1.0000e-05
Epoch 40/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1617 - accuracy: 0.9994 - val_loss: 0.1947 - val_accuracy: 0.9403 - lr: 1.0000e-05
Epoch 41/100
71/71 [==============================] - 3s 47ms/step - loss: 0.1617 - accuracy: 0.9991 - val_loss: 0.2031 - val_accuracy: 0.9397 - lr: 1.0000e-05
Epoch 42/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1625 - accuracy: 0.9994 - val_loss: 0.2011 - val_accuracy: 0.9397 - lr: 1.0000e-06
94/94 [==============================] - 1s 4ms/step
Cohen’s Kappa Score: 0.9510714285714286
Running CNN2
Epoch 1/100
71/71 [==============================] - 19s 257ms/step - loss: 1.7878 - accuracy: 0.4587 - val_loss: 4.0731 - val_accuracy: 0.0987 - lr: 0.0010
Epoch 2/100
71/71 [==============================] - 18s 253ms/step - loss: 1.1878 - accuracy: 0.6583 - val_loss: 5.1206 - val_accuracy: 0.1483 - lr: 0.0010
Epoch 3/100
71/71 [==============================] - 18s 254ms/step - loss: 0.9155 - accuracy: 0.7545 - val_loss: 4.5687 - val_accuracy: 0.2820 - lr: 0.0010
Epoch 4/100
71/71 [==============================] - 18s 254ms/step - loss: 0.7063 - accuracy: 0.8220 - val_loss: 1.8533 - val_accuracy: 0.5067 - lr: 0.0010
Epoch 5/100
71/71 [==============================] - 18s 254ms/step - loss: 0.6006 - accuracy: 0.8556 - val_loss: 2.2554 - val_accuracy: 0.4253 - lr: 0.0010
Epoch 6/100
71/71 [==============================] - 18s 254ms/step - loss: 0.5081 - accuracy: 0.8889 - val_loss: 3.2624 - val_accuracy: 0.4637 - lr: 0.0010
Epoch 7/100
71/71 [==============================] - 18s 254ms/step - loss: 0.4479 - accuracy: 0.9098 - val_loss: 1.3525 - val_accuracy: 0.6073 - lr: 0.0010
Epoch 8/100
71/71 [==============================] - 18s 254ms/step - loss: 0.4045 - accuracy: 0.9233 - val_loss: 2.1651 - val_accuracy: 0.4577 - lr: 0.0010
Epoch 9/100
71/71 [==============================] - 18s 254ms/step - loss: 0.3781 - accuracy: 0.9380 - val_loss: 2.5053 - val_accuracy: 0.3750 - lr: 0.0010
Epoch 10/100
71/71 [==============================] - 18s 254ms/step - loss: 0.3464 - accuracy: 0.9468 - val_loss: 0.8032 - val_accuracy: 0.7140 - lr: 0.0010
Epoch 11/100
71/71 [==============================] - 18s 254ms/step - loss: 0.3256 - accuracy: 0.9533 - val_loss: 0.7822 - val_accuracy: 0.7643 - lr: 0.0010
Epoch 12/100
71/71 [==============================] - 18s 254ms/step - loss: 0.3040 - accuracy: 0.9578 - val_loss: 1.6804 - val_accuracy: 0.6890 - lr: 0.0010
Epoch 13/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2898 - accuracy: 0.9640 - val_loss: 2.8188 - val_accuracy: 0.4077 - lr: 0.0010
Epoch 14/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2721 - accuracy: 0.9693 - val_loss: 4.6301 - val_accuracy: 0.3590 - lr: 0.0010
Epoch 15/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2645 - accuracy: 0.9735 - val_loss: 0.7805 - val_accuracy: 0.7577 - lr: 0.0010
Epoch 16/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2440 - accuracy: 0.9803 - val_loss: 1.3518 - val_accuracy: 0.6323 - lr: 0.0010
Epoch 17/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2388 - accuracy: 0.9818 - val_loss: 0.4260 - val_accuracy: 0.8790 - lr: 0.0010
Epoch 18/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2506 - accuracy: 0.9757 - val_loss: 0.7564 - val_accuracy: 0.7567 - lr: 0.0010
Epoch 19/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2279 - accuracy: 0.9838 - val_loss: 2.2328 - val_accuracy: 0.5347 - lr: 0.0010
Epoch 20/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2298 - accuracy: 0.9828 - val_loss: 0.7749 - val_accuracy: 0.7757 - lr: 0.0010
Epoch 21/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2244 - accuracy: 0.9839 - val_loss: 0.4375 - val_accuracy: 0.8803 - lr: 0.0010
Epoch 22/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2107 - accuracy: 0.9886 - val_loss: 0.2771 - val_accuracy: 0.9187 - lr: 0.0010
Epoch 23/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2021 - accuracy: 0.9906 - val_loss: 0.8245 - val_accuracy: 0.7517 - lr: 0.0010
Epoch 24/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2057 - accuracy: 0.9894 - val_loss: 0.2404 - val_accuracy: 0.9307 - lr: 0.0010
Epoch 25/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2060 - accuracy: 0.9889 - val_loss: 1.0036 - val_accuracy: 0.7393 - lr: 0.0010
Epoch 26/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1999 - accuracy: 0.9912 - val_loss: 0.4259 - val_accuracy: 0.8793 - lr: 0.0010
Epoch 27/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1857 - accuracy: 0.9947 - val_loss: 1.4373 - val_accuracy: 0.6787 - lr: 0.0010
Epoch 28/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2016 - accuracy: 0.9888 - val_loss: 0.5908 - val_accuracy: 0.8473 - lr: 0.0010
Epoch 29/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1914 - accuracy: 0.9928 - val_loss: 0.4179 - val_accuracy: 0.8927 - lr: 0.0010
Epoch 30/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1720 - accuracy: 0.9979 - val_loss: 0.0722 - val_accuracy: 0.9897 - lr: 1.0000e-04
Epoch 31/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1636 - accuracy: 0.9984 - val_loss: 0.0673 - val_accuracy: 0.9907 - lr: 1.0000e-04
Epoch 32/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1632 - accuracy: 0.9988 - val_loss: 0.0700 - val_accuracy: 0.9890 - lr: 1.0000e-04
Epoch 33/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1612 - accuracy: 0.9988 - val_loss: 0.0641 - val_accuracy: 0.9883 - lr: 1.0000e-04
Epoch 34/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1611 - accuracy: 0.9992 - val_loss: 0.0592 - val_accuracy: 0.9900 - lr: 1.0000e-04
Epoch 35/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1612 - accuracy: 0.9989 - val_loss: 0.0574 - val_accuracy: 0.9907 - lr: 1.0000e-04
Epoch 36/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1594 - accuracy: 0.9993 - val_loss: 0.0624 - val_accuracy: 0.9903 - lr: 1.0000e-04
Epoch 37/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1591 - accuracy: 0.9992 - val_loss: 0.0639 - val_accuracy: 0.9907 - lr: 1.0000e-04
Epoch 38/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1585 - accuracy: 0.9994 - val_loss: 0.0549 - val_accuracy: 0.9907 - lr: 1.0000e-04
Epoch 39/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1602 - accuracy: 0.9992 - val_loss: 0.0611 - val_accuracy: 0.9903 - lr: 1.0000e-04
Epoch 40/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1596 - accuracy: 0.9989 - val_loss: 0.0600 - val_accuracy: 0.9920 - lr: 1.0000e-04
Epoch 41/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1590 - accuracy: 0.9990 - val_loss: 0.0574 - val_accuracy: 0.9903 - lr: 1.0000e-04
Epoch 42/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1578 - accuracy: 0.9993 - val_loss: 0.0556 - val_accuracy: 0.9923 - lr: 1.0000e-04
Epoch 43/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1572 - accuracy: 0.9993 - val_loss: 0.0581 - val_accuracy: 0.9910 - lr: 1.0000e-04
Epoch 44/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1558 - accuracy: 0.9998 - val_loss: 0.0519 - val_accuracy: 0.9920 - lr: 1.0000e-05
Epoch 45/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1556 - accuracy: 0.9997 - val_loss: 0.0497 - val_accuracy: 0.9913 - lr: 1.0000e-05
Epoch 46/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1543 - accuracy: 0.9996 - val_loss: 0.0497 - val_accuracy: 0.9917 - lr: 1.0000e-05
Epoch 47/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1564 - accuracy: 0.9994 - val_loss: 0.0498 - val_accuracy: 0.9917 - lr: 1.0000e-05
Epoch 48/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1548 - accuracy: 0.9992 - val_loss: 0.0496 - val_accuracy: 0.9920 - lr: 1.0000e-05
Epoch 49/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1534 - accuracy: 0.9997 - val_loss: 0.0491 - val_accuracy: 0.9920 - lr: 1.0000e-05
Epoch 50/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1553 - accuracy: 0.9994 - val_loss: 0.0506 - val_accuracy: 0.9917 - lr: 1.0000e-05
Epoch 51/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1548 - accuracy: 0.9994 - val_loss: 0.0501 - val_accuracy: 0.9920 - lr: 1.0000e-05
Epoch 52/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1552 - accuracy: 0.9996 - val_loss: 0.0498 - val_accuracy: 0.9923 - lr: 1.0000e-05
94/94 [==============================] - 2s 16ms/step
Cohen’s Kappa Score: 0.9907142857142857
Running VGG_Baseline
Epoch 1/100
71/71 [==============================] - 6s 65ms/step - loss: 3.9520 - accuracy: 0.2846 - val_loss: 7.1408 - val_accuracy: 0.0770 - lr: 0.0100
Epoch 2/100
71/71 [==============================] - 4s 59ms/step - loss: 2.3396 - accuracy: 0.4279 - val_loss: 7.3036 - val_accuracy: 0.0740 - lr: 0.0100
Epoch 3/100
71/71 [==============================] - 4s 59ms/step - loss: 2.0839 - accuracy: 0.4915 - val_loss: 4.7925 - val_accuracy: 0.0863 - lr: 0.0100
Epoch 4/100
71/71 [==============================] - 4s 59ms/step - loss: 1.9794 - accuracy: 0.5269 - val_loss: 3.1535 - val_accuracy: 0.1473 - lr: 0.0100
Epoch 5/100
71/71 [==============================] - 4s 58ms/step - loss: 1.7999 - accuracy: 0.5850 - val_loss: 8.2560 - val_accuracy: 0.0573 - lr: 0.0100
Epoch 6/100
71/71 [==============================] - 4s 58ms/step - loss: 1.7733 - accuracy: 0.5974 - val_loss: 4.9414 - val_accuracy: 0.0677 - lr: 0.0100
Epoch 7/100
71/71 [==============================] - 4s 58ms/step - loss: 1.6442 - accuracy: 0.6539 - val_loss: 6.0240 - val_accuracy: 0.0907 - lr: 0.0100
Epoch 8/100
71/71 [==============================] - 4s 58ms/step - loss: 1.5646 - accuracy: 0.6693 - val_loss: 3.7842 - val_accuracy: 0.1440 - lr: 0.0100
Epoch 9/100
71/71 [==============================] - 4s 59ms/step - loss: 1.4531 - accuracy: 0.7086 - val_loss: 3.9984 - val_accuracy: 0.2040 - lr: 0.0100
Epoch 10/100
71/71 [==============================] - 4s 59ms/step - loss: 1.1131 - accuracy: 0.8107 - val_loss: 2.8915 - val_accuracy: 0.2390 - lr: 1.0000e-03
Epoch 11/100
71/71 [==============================] - 4s 59ms/step - loss: 0.9129 - accuracy: 0.8522 - val_loss: 2.3875 - val_accuracy: 0.2973 - lr: 1.0000e-03
Epoch 12/100
71/71 [==============================] - 4s 59ms/step - loss: 0.8237 - accuracy: 0.8748 - val_loss: 2.2035 - val_accuracy: 0.3167 - lr: 1.0000e-03
Epoch 13/100
71/71 [==============================] - 4s 58ms/step - loss: 0.7508 - accuracy: 0.8865 - val_loss: 2.3202 - val_accuracy: 0.3017 - lr: 1.0000e-03
Epoch 14/100
71/71 [==============================] - 4s 58ms/step - loss: 0.6863 - accuracy: 0.9042 - val_loss: 2.1940 - val_accuracy: 0.2853 - lr: 1.0000e-03
Epoch 15/100
71/71 [==============================] - 4s 59ms/step - loss: 0.6453 - accuracy: 0.9165 - val_loss: 2.0312 - val_accuracy: 0.3660 - lr: 1.0000e-03
Epoch 16/100
71/71 [==============================] - 4s 59ms/step - loss: 0.6132 - accuracy: 0.9238 - val_loss: 1.7689 - val_accuracy: 0.4973 - lr: 1.0000e-03
Epoch 17/100
71/71 [==============================] - 4s 59ms/step - loss: 0.5879 - accuracy: 0.9315 - val_loss: 1.5576 - val_accuracy: 0.5430 - lr: 1.0000e-03
Epoch 18/100
71/71 [==============================] - 4s 59ms/step - loss: 0.5633 - accuracy: 0.9385 - val_loss: 1.1693 - val_accuracy: 0.6370 - lr: 1.0000e-03
Epoch 19/100
71/71 [==============================] - 4s 58ms/step - loss: 0.5197 - accuracy: 0.9484 - val_loss: 1.4228 - val_accuracy: 0.5770 - lr: 1.0000e-03
Epoch 20/100
71/71 [==============================] - 4s 58ms/step - loss: 0.5020 - accuracy: 0.9548 - val_loss: 1.6494 - val_accuracy: 0.5170 - lr: 1.0000e-03
Epoch 21/100
71/71 [==============================] - 4s 59ms/step - loss: 0.5114 - accuracy: 0.9466 - val_loss: 0.9779 - val_accuracy: 0.7420 - lr: 1.0000e-03
Epoch 22/100
71/71 [==============================] - 4s 58ms/step - loss: 0.4705 - accuracy: 0.9616 - val_loss: 0.9594 - val_accuracy: 0.7287 - lr: 1.0000e-03
Epoch 23/100
71/71 [==============================] - 4s 58ms/step - loss: 0.4707 - accuracy: 0.9609 - val_loss: 0.9918 - val_accuracy: 0.7350 - lr: 1.0000e-03
Epoch 24/100
71/71 [==============================] - 4s 59ms/step - loss: 0.4676 - accuracy: 0.9589 - val_loss: 0.8780 - val_accuracy: 0.7703 - lr: 1.0000e-03
Epoch 25/100
71/71 [==============================] - 4s 58ms/step - loss: 0.4302 - accuracy: 0.9701 - val_loss: 1.0563 - val_accuracy: 0.7163 - lr: 1.0000e-03
Epoch 26/100
71/71 [==============================] - 4s 58ms/step - loss: 0.4041 - accuracy: 0.9777 - val_loss: 1.1323 - val_accuracy: 0.6427 - lr: 1.0000e-03
Epoch 27/100
71/71 [==============================] - 4s 58ms/step - loss: 0.3991 - accuracy: 0.9764 - val_loss: 1.0908 - val_accuracy: 0.6693 - lr: 1.0000e-03
Epoch 28/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3942 - accuracy: 0.9776 - val_loss: 0.7115 - val_accuracy: 0.8223 - lr: 1.0000e-03
Epoch 29/100
71/71 [==============================] - 4s 58ms/step - loss: 0.3892 - accuracy: 0.9770 - val_loss: 1.2553 - val_accuracy: 0.6257 - lr: 1.0000e-03
Epoch 30/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3763 - accuracy: 0.9792 - val_loss: 1.2234 - val_accuracy: 0.6350 - lr: 1.0000e-03
Epoch 31/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3709 - accuracy: 0.9821 - val_loss: 0.7015 - val_accuracy: 0.8110 - lr: 1.0000e-03
Epoch 32/100
71/71 [==============================] - 4s 58ms/step - loss: 0.3900 - accuracy: 0.9746 - val_loss: 0.9373 - val_accuracy: 0.7887 - lr: 1.0000e-03
Epoch 33/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3748 - accuracy: 0.9785 - val_loss: 0.5147 - val_accuracy: 0.8863 - lr: 1.0000e-03
Epoch 34/100
71/71 [==============================] - 4s 58ms/step - loss: 0.3625 - accuracy: 0.9813 - val_loss: 0.7600 - val_accuracy: 0.7913 - lr: 1.0000e-03
Epoch 35/100
71/71 [==============================] - 4s 58ms/step - loss: 0.3564 - accuracy: 0.9822 - val_loss: 0.8401 - val_accuracy: 0.7590 - lr: 1.0000e-03
Epoch 36/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3400 - accuracy: 0.9868 - val_loss: 0.8195 - val_accuracy: 0.7657 - lr: 1.0000e-03
Epoch 37/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3415 - accuracy: 0.9848 - val_loss: 0.4422 - val_accuracy: 0.9000 - lr: 1.0000e-03
Epoch 38/100
71/71 [==============================] - 4s 58ms/step - loss: 0.3278 - accuracy: 0.9889 - val_loss: 0.7631 - val_accuracy: 0.7917 - lr: 1.0000e-03
Epoch 39/100
71/71 [==============================] - 4s 58ms/step - loss: 0.3382 - accuracy: 0.9876 - val_loss: 0.5782 - val_accuracy: 0.8523 - lr: 1.0000e-03
Epoch 40/100
71/71 [==============================] - 4s 58ms/step - loss: 0.3332 - accuracy: 0.9855 - val_loss: 1.6601 - val_accuracy: 0.5307 - lr: 1.0000e-03
Epoch 41/100
71/71 [==============================] - 4s 58ms/step - loss: 0.3257 - accuracy: 0.9872 - val_loss: 0.7446 - val_accuracy: 0.7850 - lr: 1.0000e-03
Epoch 42/100
71/71 [==============================] - 4s 58ms/step - loss: 0.3173 - accuracy: 0.9897 - val_loss: 0.5939 - val_accuracy: 0.8440 - lr: 1.0000e-03
Epoch 43/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2896 - accuracy: 0.9976 - val_loss: 0.2230 - val_accuracy: 0.9797 - lr: 1.0000e-04
Epoch 44/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2741 - accuracy: 0.9990 - val_loss: 0.1817 - val_accuracy: 0.9900 - lr: 1.0000e-04
Epoch 45/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2711 - accuracy: 0.9994 - val_loss: 0.1700 - val_accuracy: 0.9927 - lr: 1.0000e-04
Epoch 46/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2692 - accuracy: 0.9994 - val_loss: 0.1648 - val_accuracy: 0.9930 - lr: 1.0000e-04
Epoch 47/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2652 - accuracy: 0.9996 - val_loss: 0.1620 - val_accuracy: 0.9923 - lr: 1.0000e-04
Epoch 48/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2629 - accuracy: 0.9998 - val_loss: 0.1576 - val_accuracy: 0.9933 - lr: 1.0000e-04
Epoch 49/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2604 - accuracy: 0.9998 - val_loss: 0.1589 - val_accuracy: 0.9933 - lr: 1.0000e-04
Epoch 50/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2574 - accuracy: 0.9999 - val_loss: 0.1551 - val_accuracy: 0.9930 - lr: 1.0000e-04
Epoch 51/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2553 - accuracy: 0.9999 - val_loss: 0.1503 - val_accuracy: 0.9937 - lr: 1.0000e-04
Epoch 52/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2536 - accuracy: 0.9997 - val_loss: 0.1540 - val_accuracy: 0.9930 - lr: 1.0000e-04
Epoch 53/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2509 - accuracy: 0.9999 - val_loss: 0.1509 - val_accuracy: 0.9933 - lr: 1.0000e-04
Epoch 54/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2503 - accuracy: 0.9997 - val_loss: 0.1488 - val_accuracy: 0.9937 - lr: 1.0000e-04
Epoch 55/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2483 - accuracy: 1.0000 - val_loss: 0.1486 - val_accuracy: 0.9927 - lr: 1.0000e-04
Epoch 56/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2472 - accuracy: 0.9999 - val_loss: 0.1450 - val_accuracy: 0.9923 - lr: 1.0000e-04
Epoch 57/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2448 - accuracy: 0.9996 - val_loss: 0.1458 - val_accuracy: 0.9927 - lr: 1.0000e-04
Epoch 58/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2438 - accuracy: 0.9998 - val_loss: 0.1466 - val_accuracy: 0.9933 - lr: 1.0000e-04
Epoch 59/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2416 - accuracy: 1.0000 - val_loss: 0.1441 - val_accuracy: 0.9923 - lr: 1.0000e-04
Epoch 60/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2380 - accuracy: 1.0000 - val_loss: 0.1464 - val_accuracy: 0.9937 - lr: 1.0000e-04
Epoch 61/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2382 - accuracy: 0.9999 - val_loss: 0.1415 - val_accuracy: 0.9943 - lr: 1.0000e-04
Epoch 62/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2361 - accuracy: 1.0000 - val_loss: 0.1417 - val_accuracy: 0.9927 - lr: 1.0000e-04
Epoch 63/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2348 - accuracy: 0.9999 - val_loss: 0.1453 - val_accuracy: 0.9937 - lr: 1.0000e-04
Epoch 64/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2326 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9933 - lr: 1.0000e-04
Epoch 65/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2306 - accuracy: 1.0000 - val_loss: 0.1398 - val_accuracy: 0.9930 - lr: 1.0000e-04
Epoch 66/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2300 - accuracy: 1.0000 - val_loss: 0.1386 - val_accuracy: 0.9930 - lr: 1.0000e-04
Epoch 67/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2290 - accuracy: 0.9998 - val_loss: 0.1383 - val_accuracy: 0.9920 - lr: 1.0000e-04
Epoch 68/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2279 - accuracy: 0.9998 - val_loss: 0.1431 - val_accuracy: 0.9913 - lr: 1.0000e-04
Epoch 69/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2256 - accuracy: 0.9999 - val_loss: 0.1328 - val_accuracy: 0.9920 - lr: 1.0000e-04
Epoch 70/100
71/71 [==============================] - 4s 58ms/step - loss: 0.2258 - accuracy: 0.9998 - val_loss: 0.1388 - val_accuracy: 0.9903 - lr: 1.0000e-04
Epoch 71/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2229 - accuracy: 0.9999 - val_loss: 0.1283 - val_accuracy: 0.9927 - lr: 1.0000e-04
94/94 [==============================] - 1s 5ms/step
Cohen’s Kappa Score: 0.9914285714285714
for fig in figures_cutmix:
fig()
overall.iloc[-3:]
| Model Name | Epochs | Batch Size | Train Loss | Test Loss | Train Acc | Test Acc | Kappa | Comments | |
|---|---|---|---|---|---|---|---|---|---|
| 6 | CNN cutmix | 42 | 128 | 0.164451 | 0.178881 | 0.998892 | 0.945667 | 0.951071 | NaN |
| 7 | CNN2 cutmix | 52 | 128 | 0.157783 | 0.055629 | 0.999335 | 0.992333 | 0.990714 | NaN |
| 8 | VGG_Baseline cutmix | 71 | 128 | 0.238155 | 0.141491 | 0.999889 | 0.994333 | 0.991429 | NaN |
def cutout(image, n_holes, length):
height = tf.shape(image)[0]
width = tf.shape(image)[1]
mask = tf.ones((height, width), dtype=tf.float32)
for _ in range(n_holes):
y = tf.random.uniform([], 0, height, dtype=tf.int32)
x = tf.random.uniform([], 0, width, dtype=tf.int32)
y1 = tf.cast(tf.clip_by_value(y - length // 2, 0, height), tf.int32)
y2 = tf.cast(tf.clip_by_value(y + length // 2, 0, height), tf.int32)
x1 = tf.cast(tf.clip_by_value(x - length // 2, 0, width), tf.int32)
x2 = tf.cast(tf.clip_by_value(x + length // 2, 0, width), tf.int32)
y_mask = (tf.range(height)[:, None] >= y1) & (tf.range(height)[:, None] < y2)
x_mask = (tf.range(width)[None, :] >= x1) & (tf.range(width)[None, :] < x2)
cutout_mask = y_mask & x_mask
mask = tf.where(cutout_mask, tf.zeros_like(mask), mask)
mask = tf.expand_dims(mask, -1) # Expanding to 3D to match image dimensions
mask = tf.tile(mask, [1, 1, tf.shape(image)[-1]]) # Adjusting channels to match the image
return image * mask
train_ds_one = tf.data.Dataset.from_tensor_slices((X_train_big, y_train_big)).shuffle(2048).map(preprocess_data, num_parallel_calls=tf.data.AUTOTUNE)
train_ds_two = tf.data.Dataset.from_tensor_slices((X_train_big, y_train_big)).shuffle(2048).map(preprocess_data, num_parallel_calls=tf.data.AUTOTUNE)
val_ds_cutout = tf.data.Dataset.from_tensor_slices((X_val_big, y_val_big))
val_ds_cutout = val_ds_cutout.map(preprocess_data, num_parallel_calls=tf.data.AUTOTUNE)
val_ds_cutout = val_ds_cutout.map(lambda x, y: (cutout(x, n_holes=1, length=16), y), num_parallel_calls=tf.data.AUTOTUNE)
val_ds_cutout = val_ds_cutout.batch(128).prefetch(tf.data.AUTOTUNE)
train_ds_cutout = train_ds_one.map(lambda x, y: (cutout(x, n_holes=1, length=16), y), num_parallel_calls=tf.data.AUTOTUNE)
train_ds_cutout = train_ds_cutout.batch(128).prefetch(tf.data.AUTOTUNE)
image_batch, label_batch = next(iter(train_ds_cutout))
plt.figure(figsize=(10, 10))
for i in range(15):
ax = plt.subplot(3, 5, i + 1)
plt.title(labels_dict[np.argmax(label_batch[i])])
plt.imshow(tf.squeeze(image_batch[i]), cmap="gray")
plt.axis("off")
plt.show()
models = models_array()
figures_cutout = []
for i in range(len(models)):
print(f'Running {model_names[i]}')
results, fig = evaluator.model_evaluate( train_ds_cutout, val_ds_cutout , models[i], base_hparams)
results['Model Name'] = f'{model_names[i]} cutout'
y_pred = models[i].predict(X_test_big)
y_pred_classes = np.argmax(y_pred, axis=1)
kappa = cohen_kappa_score(y_test_big, y_pred_classes)
print("Cohen’s Kappa Score:", kappa)
results['Kappa'] = kappa
overall = pd.concat([overall, pd.DataFrame([results])], ignore_index=True)
figures_cutout.append(fig)
Model: "sequential_16"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_11 (Conv2D) (None, 128, 128, 32) 320
batch_normalization_11 (Bat (None, 128, 128, 32) 128
chNormalization)
re_lu_11 (ReLU) (None, 128, 128, 32) 0
conv2d_12 (Conv2D) (None, 128, 128, 64) 18496
batch_normalization_12 (Bat (None, 128, 128, 64) 256
chNormalization)
re_lu_12 (ReLU) (None, 128, 128, 64) 0
conv2d_13 (Conv2D) (None, 128, 128, 128) 73856
batch_normalization_13 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_13 (ReLU) (None, 128, 128, 128) 0
conv2d_14 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_14 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_14 (ReLU) (None, 128, 128, 128) 0
conv2d_15 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_15 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_15 (ReLU) (None, 128, 128, 128) 0
conv2d_16 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_16 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_16 (ReLU) (None, 128, 128, 128) 0
global_average_pooling2d_1 (None, 128) 0
(GlobalAveragePooling2D)
dense_1 (Dense) (None, 256) 33024
dense_2 (Dense) (None, 15) 3855
=================================================================
Total params: 574,735
Trainable params: 573,519
Non-trainable params: 1,216
_________________________________________________________________
Model: "CNN_Baseline"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_17 (Conv2D) (None, 128, 128, 64) 640
batch_normalization_17 (Bat (None, 128, 128, 64) 256
chNormalization)
max_pooling2d_4 (MaxPooling (None, 64, 64, 64) 0
2D)
dropout_1 (Dropout) (None, 64, 64, 64) 0
conv2d_18 (Conv2D) (None, 64, 64, 128) 73856
batch_normalization_18 (Bat (None, 64, 64, 128) 512
chNormalization)
max_pooling2d_5 (MaxPooling (None, 32, 32, 128) 0
2D)
dropout_2 (Dropout) (None, 32, 32, 128) 0
conv2d_19 (Conv2D) (None, 32, 32, 256) 295168
batch_normalization_19 (Bat (None, 32, 32, 256) 1024
chNormalization)
max_pooling2d_6 (MaxPooling (None, 16, 16, 256) 0
2D)
dropout_3 (Dropout) (None, 16, 16, 256) 0
flatten (Flatten) (None, 65536) 0
dense_3 (Dense) (None, 512) 33554944
batch_normalization_20 (Bat (None, 512) 2048
chNormalization)
dropout_4 (Dropout) (None, 512) 0
dense_4 (Dense) (None, 128) 65664
batch_normalization_21 (Bat (None, 128) 512
chNormalization)
dropout_5 (Dropout) (None, 128) 0
dense_5 (Dense) (None, 15) 1935
=================================================================
Total params: 33,996,559
Trainable params: 33,994,383
Non-trainable params: 2,176
_________________________________________________________________
Running CNN
Epoch 1/100
71/71 [==============================] - 5s 57ms/step - loss: 2.2031 - accuracy: 0.3745 - val_loss: 6.4476 - val_accuracy: 0.1113 - lr: 0.0010
Epoch 2/100
71/71 [==============================] - 4s 50ms/step - loss: 1.2053 - accuracy: 0.6276 - val_loss: 2.0152 - val_accuracy: 0.3620 - lr: 0.0010
Epoch 3/100
71/71 [==============================] - 4s 50ms/step - loss: 0.8698 - accuracy: 0.7292 - val_loss: 1.0972 - val_accuracy: 0.6617 - lr: 0.0010
Epoch 4/100
71/71 [==============================] - 3s 49ms/step - loss: 0.6079 - accuracy: 0.8074 - val_loss: 1.3297 - val_accuracy: 0.5957 - lr: 0.0010
Epoch 5/100
71/71 [==============================] - 3s 48ms/step - loss: 0.4422 - accuracy: 0.8634 - val_loss: 1.4359 - val_accuracy: 0.6093 - lr: 0.0010
Epoch 6/100
71/71 [==============================] - 4s 50ms/step - loss: 0.3258 - accuracy: 0.9018 - val_loss: 0.8552 - val_accuracy: 0.7497 - lr: 0.0010
Epoch 7/100
71/71 [==============================] - 4s 50ms/step - loss: 0.2399 - accuracy: 0.9286 - val_loss: 0.7387 - val_accuracy: 0.7867 - lr: 0.0010
Epoch 8/100
71/71 [==============================] - 4s 50ms/step - loss: 0.1641 - accuracy: 0.9529 - val_loss: 0.4909 - val_accuracy: 0.8527 - lr: 0.0010
Epoch 9/100
71/71 [==============================] - 3s 49ms/step - loss: 0.1443 - accuracy: 0.9599 - val_loss: 0.6127 - val_accuracy: 0.8147 - lr: 0.0010
Epoch 10/100
71/71 [==============================] - 3s 49ms/step - loss: 0.1087 - accuracy: 0.9712 - val_loss: 1.0661 - val_accuracy: 0.7373 - lr: 0.0010
Epoch 11/100
71/71 [==============================] - 4s 50ms/step - loss: 0.0857 - accuracy: 0.9766 - val_loss: 0.4850 - val_accuracy: 0.8633 - lr: 0.0010
Epoch 12/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0872 - accuracy: 0.9744 - val_loss: 0.9205 - val_accuracy: 0.7743 - lr: 0.0010
Epoch 13/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0644 - accuracy: 0.9836 - val_loss: 0.5137 - val_accuracy: 0.8530 - lr: 0.0010
Epoch 14/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0765 - accuracy: 0.9788 - val_loss: 0.8142 - val_accuracy: 0.7743 - lr: 0.0010
Epoch 15/100
71/71 [==============================] - 4s 50ms/step - loss: 0.0619 - accuracy: 0.9824 - val_loss: 0.3808 - val_accuracy: 0.8967 - lr: 0.0010
Epoch 16/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0512 - accuracy: 0.9869 - val_loss: 0.9921 - val_accuracy: 0.7680 - lr: 0.0010
Epoch 17/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0457 - accuracy: 0.9874 - val_loss: 0.9095 - val_accuracy: 0.7747 - lr: 0.0010
Epoch 18/100
71/71 [==============================] - 4s 50ms/step - loss: 0.0446 - accuracy: 0.9872 - val_loss: 0.2453 - val_accuracy: 0.9267 - lr: 0.0010
Epoch 19/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0438 - accuracy: 0.9856 - val_loss: 0.5057 - val_accuracy: 0.8583 - lr: 0.0010
Epoch 20/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0485 - accuracy: 0.9846 - val_loss: 0.5884 - val_accuracy: 0.8557 - lr: 0.0010
Epoch 21/100
71/71 [==============================] - 4s 50ms/step - loss: 0.0353 - accuracy: 0.9906 - val_loss: 0.2665 - val_accuracy: 0.9320 - lr: 0.0010
Epoch 22/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0335 - accuracy: 0.9909 - val_loss: 2.9167 - val_accuracy: 0.6673 - lr: 0.0010
Epoch 23/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0345 - accuracy: 0.9897 - val_loss: 1.0126 - val_accuracy: 0.7557 - lr: 0.0010
Epoch 24/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0264 - accuracy: 0.9928 - val_loss: 0.3340 - val_accuracy: 0.9160 - lr: 1.0000e-04
Epoch 25/100
71/71 [==============================] - 4s 50ms/step - loss: 0.0191 - accuracy: 0.9950 - val_loss: 0.2300 - val_accuracy: 0.9390 - lr: 1.0000e-04
Epoch 26/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0204 - accuracy: 0.9941 - val_loss: 0.2156 - val_accuracy: 0.9383 - lr: 1.0000e-04
Epoch 27/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0164 - accuracy: 0.9963 - val_loss: 0.2243 - val_accuracy: 0.9387 - lr: 1.0000e-04
Epoch 28/100
71/71 [==============================] - 4s 50ms/step - loss: 0.0153 - accuracy: 0.9962 - val_loss: 0.2185 - val_accuracy: 0.9420 - lr: 1.0000e-04
Epoch 29/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0129 - accuracy: 0.9977 - val_loss: 0.3506 - val_accuracy: 0.9127 - lr: 1.0000e-04
Epoch 30/100
71/71 [==============================] - 4s 50ms/step - loss: 0.0118 - accuracy: 0.9977 - val_loss: 0.1980 - val_accuracy: 0.9447 - lr: 1.0000e-04
Epoch 31/100
71/71 [==============================] - 4s 50ms/step - loss: 0.0117 - accuracy: 0.9970 - val_loss: 0.1892 - val_accuracy: 0.9493 - lr: 1.0000e-04
Epoch 32/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0113 - accuracy: 0.9975 - val_loss: 0.3271 - val_accuracy: 0.9200 - lr: 1.0000e-04
Epoch 33/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0121 - accuracy: 0.9981 - val_loss: 0.2797 - val_accuracy: 0.9277 - lr: 1.0000e-04
Epoch 34/100
71/71 [==============================] - 4s 50ms/step - loss: 0.0097 - accuracy: 0.9977 - val_loss: 0.1812 - val_accuracy: 0.9517 - lr: 1.0000e-04
Epoch 35/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0086 - accuracy: 0.9986 - val_loss: 0.1981 - val_accuracy: 0.9467 - lr: 1.0000e-04
Epoch 36/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0108 - accuracy: 0.9979 - val_loss: 0.1902 - val_accuracy: 0.9490 - lr: 1.0000e-04
Epoch 37/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0096 - accuracy: 0.9979 - val_loss: 0.1859 - val_accuracy: 0.9510 - lr: 1.0000e-04
Epoch 38/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0103 - accuracy: 0.9972 - val_loss: 0.2028 - val_accuracy: 0.9457 - lr: 1.0000e-04
Epoch 39/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0092 - accuracy: 0.9984 - val_loss: 0.1980 - val_accuracy: 0.9457 - lr: 1.0000e-04
Epoch 40/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0083 - accuracy: 0.9982 - val_loss: 0.2020 - val_accuracy: 0.9447 - lr: 1.0000e-05
Epoch 41/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0075 - accuracy: 0.9987 - val_loss: 0.1979 - val_accuracy: 0.9470 - lr: 1.0000e-05
Epoch 42/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0089 - accuracy: 0.9986 - val_loss: 0.1893 - val_accuracy: 0.9477 - lr: 1.0000e-05
Epoch 43/100
71/71 [==============================] - 3s 49ms/step - loss: 0.0098 - accuracy: 0.9976 - val_loss: 0.2064 - val_accuracy: 0.9443 - lr: 1.0000e-05
Epoch 44/100
71/71 [==============================] - 4s 50ms/step - loss: 0.0067 - accuracy: 0.9990 - val_loss: 0.2109 - val_accuracy: 0.9433 - lr: 1.0000e-05
94/94 [==============================] - 1s 4ms/step
Cohen’s Kappa Score: 0.9564285714285714
Running CNN2
Epoch 1/100
71/71 [==============================] - 19s 257ms/step - loss: 1.7408 - accuracy: 0.4718 - val_loss: 3.3004 - val_accuracy: 0.1213 - lr: 0.0010
Epoch 2/100
71/71 [==============================] - 18s 255ms/step - loss: 1.0736 - accuracy: 0.6700 - val_loss: 3.5122 - val_accuracy: 0.2703 - lr: 0.0010
Epoch 3/100
71/71 [==============================] - 18s 254ms/step - loss: 0.7570 - accuracy: 0.7707 - val_loss: 4.6527 - val_accuracy: 0.2910 - lr: 0.0010
Epoch 4/100
71/71 [==============================] - 18s 255ms/step - loss: 0.5997 - accuracy: 0.8249 - val_loss: 3.6512 - val_accuracy: 0.3950 - lr: 0.0010
Epoch 5/100
71/71 [==============================] - 18s 254ms/step - loss: 0.4691 - accuracy: 0.8638 - val_loss: 5.0946 - val_accuracy: 0.3213 - lr: 0.0010
Epoch 6/100
71/71 [==============================] - 18s 255ms/step - loss: 0.3817 - accuracy: 0.8902 - val_loss: 0.6853 - val_accuracy: 0.7800 - lr: 0.0010
Epoch 7/100
71/71 [==============================] - 18s 254ms/step - loss: 0.3102 - accuracy: 0.9099 - val_loss: 1.6420 - val_accuracy: 0.6190 - lr: 0.0010
Epoch 8/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2686 - accuracy: 0.9228 - val_loss: 1.3555 - val_accuracy: 0.6350 - lr: 0.0010
Epoch 9/100
71/71 [==============================] - 18s 254ms/step - loss: 0.2496 - accuracy: 0.9286 - val_loss: 2.8667 - val_accuracy: 0.3923 - lr: 0.0010
Epoch 10/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1891 - accuracy: 0.9481 - val_loss: 2.0188 - val_accuracy: 0.5933 - lr: 0.0010
Epoch 11/100
71/71 [==============================] - 18s 255ms/step - loss: 0.1767 - accuracy: 0.9513 - val_loss: 4.0301 - val_accuracy: 0.4353 - lr: 0.0010
Epoch 12/100
71/71 [==============================] - 18s 254ms/step - loss: 0.1098 - accuracy: 0.9744 - val_loss: 0.1496 - val_accuracy: 0.9623 - lr: 1.0000e-04
Epoch 13/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0893 - accuracy: 0.9836 - val_loss: 0.1377 - val_accuracy: 0.9637 - lr: 1.0000e-04
Epoch 14/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0829 - accuracy: 0.9839 - val_loss: 0.1459 - val_accuracy: 0.9613 - lr: 1.0000e-04
Epoch 15/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0797 - accuracy: 0.9838 - val_loss: 0.1258 - val_accuracy: 0.9713 - lr: 1.0000e-04
Epoch 16/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0762 - accuracy: 0.9862 - val_loss: 0.1224 - val_accuracy: 0.9703 - lr: 1.0000e-04
Epoch 17/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0748 - accuracy: 0.9860 - val_loss: 0.1367 - val_accuracy: 0.9667 - lr: 1.0000e-04
Epoch 18/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0708 - accuracy: 0.9874 - val_loss: 0.1046 - val_accuracy: 0.9750 - lr: 1.0000e-04
Epoch 19/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0701 - accuracy: 0.9865 - val_loss: 0.1063 - val_accuracy: 0.9727 - lr: 1.0000e-04
Epoch 20/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0679 - accuracy: 0.9862 - val_loss: 0.1102 - val_accuracy: 0.9707 - lr: 1.0000e-04
Epoch 21/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0635 - accuracy: 0.9885 - val_loss: 0.1141 - val_accuracy: 0.9727 - lr: 1.0000e-04
Epoch 22/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0621 - accuracy: 0.9886 - val_loss: 0.0974 - val_accuracy: 0.9760 - lr: 1.0000e-04
Epoch 23/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0611 - accuracy: 0.9876 - val_loss: 0.1439 - val_accuracy: 0.9617 - lr: 1.0000e-04
Epoch 24/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0600 - accuracy: 0.9887 - val_loss: 0.1088 - val_accuracy: 0.9717 - lr: 1.0000e-04
Epoch 25/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0577 - accuracy: 0.9893 - val_loss: 0.1088 - val_accuracy: 0.9740 - lr: 1.0000e-04
Epoch 26/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0553 - accuracy: 0.9894 - val_loss: 0.1118 - val_accuracy: 0.9700 - lr: 1.0000e-04
Epoch 27/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0531 - accuracy: 0.9907 - val_loss: 0.1011 - val_accuracy: 0.9700 - lr: 1.0000e-04
Epoch 28/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0481 - accuracy: 0.9917 - val_loss: 0.0759 - val_accuracy: 0.9837 - lr: 1.0000e-05
Epoch 29/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0452 - accuracy: 0.9932 - val_loss: 0.0736 - val_accuracy: 0.9820 - lr: 1.0000e-05
Epoch 30/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0449 - accuracy: 0.9927 - val_loss: 0.0702 - val_accuracy: 0.9833 - lr: 1.0000e-05
Epoch 31/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0458 - accuracy: 0.9921 - val_loss: 0.0729 - val_accuracy: 0.9823 - lr: 1.0000e-05
Epoch 32/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0453 - accuracy: 0.9930 - val_loss: 0.0701 - val_accuracy: 0.9817 - lr: 1.0000e-05
Epoch 33/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0433 - accuracy: 0.9941 - val_loss: 0.0712 - val_accuracy: 0.9833 - lr: 1.0000e-05
Epoch 34/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0427 - accuracy: 0.9945 - val_loss: 0.0690 - val_accuracy: 0.9817 - lr: 1.0000e-05
Epoch 35/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0421 - accuracy: 0.9940 - val_loss: 0.0700 - val_accuracy: 0.9840 - lr: 1.0000e-05
Epoch 36/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0440 - accuracy: 0.9932 - val_loss: 0.0710 - val_accuracy: 0.9820 - lr: 1.0000e-05
Epoch 37/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0426 - accuracy: 0.9931 - val_loss: 0.0709 - val_accuracy: 0.9820 - lr: 1.0000e-05
Epoch 38/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0434 - accuracy: 0.9935 - val_loss: 0.0699 - val_accuracy: 0.9827 - lr: 1.0000e-05
Epoch 39/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0435 - accuracy: 0.9937 - val_loss: 0.0703 - val_accuracy: 0.9823 - lr: 1.0000e-05
Epoch 40/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0424 - accuracy: 0.9944 - val_loss: 0.0698 - val_accuracy: 0.9833 - lr: 1.0000e-06
Epoch 41/100
71/71 [==============================] - 18s 254ms/step - loss: 0.0419 - accuracy: 0.9936 - val_loss: 0.0692 - val_accuracy: 0.9830 - lr: 1.0000e-06
Epoch 42/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0436 - accuracy: 0.9932 - val_loss: 0.0694 - val_accuracy: 0.9820 - lr: 1.0000e-06
Epoch 43/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0418 - accuracy: 0.9947 - val_loss: 0.0696 - val_accuracy: 0.9830 - lr: 1.0000e-06
Epoch 44/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0431 - accuracy: 0.9932 - val_loss: 0.0690 - val_accuracy: 0.9830 - lr: 1.0000e-06
Epoch 45/100
71/71 [==============================] - 18s 255ms/step - loss: 0.0407 - accuracy: 0.9941 - val_loss: 0.0691 - val_accuracy: 0.9833 - lr: 1.0000e-07
94/94 [==============================] - 2s 16ms/step
Cohen’s Kappa Score: 0.9817857142857143
Running VGG_Baseline
Epoch 1/100
71/71 [==============================] - 6s 66ms/step - loss: 3.9192 - accuracy: 0.2463 - val_loss: 100.5357 - val_accuracy: 0.0550 - lr: 0.0100
Epoch 2/100
71/71 [==============================] - 4s 60ms/step - loss: 2.3469 - accuracy: 0.4101 - val_loss: 4.9706 - val_accuracy: 0.0717 - lr: 0.0100
Epoch 3/100
71/71 [==============================] - 4s 60ms/step - loss: 2.0371 - accuracy: 0.4834 - val_loss: 4.9399 - val_accuracy: 0.0930 - lr: 0.0100
Epoch 4/100
71/71 [==============================] - 4s 59ms/step - loss: 1.9283 - accuracy: 0.5030 - val_loss: 4.8601 - val_accuracy: 0.0710 - lr: 0.0100
Epoch 5/100
71/71 [==============================] - 4s 60ms/step - loss: 1.8067 - accuracy: 0.5429 - val_loss: 4.5363 - val_accuracy: 0.1243 - lr: 0.0100
Epoch 6/100
71/71 [==============================] - 4s 59ms/step - loss: 1.7625 - accuracy: 0.5696 - val_loss: 4.2994 - val_accuracy: 0.1277 - lr: 0.0100
Epoch 7/100
71/71 [==============================] - 4s 59ms/step - loss: 1.6848 - accuracy: 0.5963 - val_loss: 3.9251 - val_accuracy: 0.1127 - lr: 0.0100
Epoch 8/100
71/71 [==============================] - 4s 60ms/step - loss: 1.5963 - accuracy: 0.6226 - val_loss: 5.4944 - val_accuracy: 0.1310 - lr: 0.0100
Epoch 9/100
71/71 [==============================] - 4s 60ms/step - loss: 1.4555 - accuracy: 0.6700 - val_loss: 3.7085 - val_accuracy: 0.1437 - lr: 0.0100
Epoch 10/100
71/71 [==============================] - 4s 59ms/step - loss: 1.4006 - accuracy: 0.6928 - val_loss: 10.2228 - val_accuracy: 0.0697 - lr: 0.0100
Epoch 11/100
71/71 [==============================] - 4s 59ms/step - loss: 1.3113 - accuracy: 0.7192 - val_loss: 8.5290 - val_accuracy: 0.0763 - lr: 0.0100
Epoch 12/100
71/71 [==============================] - 4s 59ms/step - loss: 1.2486 - accuracy: 0.7275 - val_loss: 8.1643 - val_accuracy: 0.1203 - lr: 0.0100
Epoch 13/100
71/71 [==============================] - 4s 59ms/step - loss: 1.1755 - accuracy: 0.7584 - val_loss: 8.2964 - val_accuracy: 0.1103 - lr: 0.0100
Epoch 14/100
71/71 [==============================] - 4s 59ms/step - loss: 1.1242 - accuracy: 0.7686 - val_loss: 4.0917 - val_accuracy: 0.1803 - lr: 0.0100
Epoch 15/100
71/71 [==============================] - 4s 59ms/step - loss: 0.8142 - accuracy: 0.8638 - val_loss: 3.1275 - val_accuracy: 0.2473 - lr: 1.0000e-03
Epoch 16/100
71/71 [==============================] - 4s 59ms/step - loss: 0.6776 - accuracy: 0.8889 - val_loss: 2.2424 - val_accuracy: 0.3693 - lr: 1.0000e-03
Epoch 17/100
71/71 [==============================] - 4s 59ms/step - loss: 0.5991 - accuracy: 0.9032 - val_loss: 2.4636 - val_accuracy: 0.3213 - lr: 1.0000e-03
Epoch 18/100
71/71 [==============================] - 4s 59ms/step - loss: 0.5428 - accuracy: 0.9169 - val_loss: 1.7247 - val_accuracy: 0.4670 - lr: 1.0000e-03
Epoch 19/100
71/71 [==============================] - 4s 59ms/step - loss: 0.5016 - accuracy: 0.9255 - val_loss: 1.6074 - val_accuracy: 0.4960 - lr: 1.0000e-03
Epoch 20/100
71/71 [==============================] - 4s 59ms/step - loss: 0.4672 - accuracy: 0.9308 - val_loss: 1.9682 - val_accuracy: 0.4517 - lr: 1.0000e-03
Epoch 21/100
71/71 [==============================] - 4s 59ms/step - loss: 0.4274 - accuracy: 0.9410 - val_loss: 1.1978 - val_accuracy: 0.6790 - lr: 1.0000e-03
Epoch 22/100
71/71 [==============================] - 4s 59ms/step - loss: 0.4021 - accuracy: 0.9432 - val_loss: 1.1860 - val_accuracy: 0.6653 - lr: 1.0000e-03
Epoch 23/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3830 - accuracy: 0.9458 - val_loss: 1.1451 - val_accuracy: 0.6733 - lr: 1.0000e-03
Epoch 24/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3518 - accuracy: 0.9526 - val_loss: 1.2538 - val_accuracy: 0.6663 - lr: 1.0000e-03
Epoch 25/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3296 - accuracy: 0.9607 - val_loss: 1.0292 - val_accuracy: 0.7137 - lr: 1.0000e-03
Epoch 26/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3338 - accuracy: 0.9561 - val_loss: 0.8417 - val_accuracy: 0.7867 - lr: 1.0000e-03
Epoch 27/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3019 - accuracy: 0.9634 - val_loss: 0.8196 - val_accuracy: 0.7807 - lr: 1.0000e-03
Epoch 28/100
71/71 [==============================] - 4s 59ms/step - loss: 0.3133 - accuracy: 0.9561 - val_loss: 1.0286 - val_accuracy: 0.7377 - lr: 1.0000e-03
Epoch 29/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2986 - accuracy: 0.9634 - val_loss: 0.7026 - val_accuracy: 0.8097 - lr: 1.0000e-03
Epoch 30/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2845 - accuracy: 0.9657 - val_loss: 0.8499 - val_accuracy: 0.7783 - lr: 1.0000e-03
Epoch 31/100
71/71 [==============================] - 4s 60ms/step - loss: 0.2689 - accuracy: 0.9684 - val_loss: 0.5115 - val_accuracy: 0.8803 - lr: 1.0000e-03
Epoch 32/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2573 - accuracy: 0.9712 - val_loss: 0.7706 - val_accuracy: 0.8137 - lr: 1.0000e-03
Epoch 33/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2398 - accuracy: 0.9768 - val_loss: 1.8389 - val_accuracy: 0.5563 - lr: 1.0000e-03
Epoch 34/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2482 - accuracy: 0.9704 - val_loss: 0.8435 - val_accuracy: 0.7957 - lr: 1.0000e-03
Epoch 35/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2505 - accuracy: 0.9696 - val_loss: 0.5964 - val_accuracy: 0.8523 - lr: 1.0000e-03
Epoch 36/100
71/71 [==============================] - 4s 59ms/step - loss: 0.2368 - accuracy: 0.9740 - val_loss: 0.8071 - val_accuracy: 0.7947 - lr: 1.0000e-03
Epoch 37/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1987 - accuracy: 0.9862 - val_loss: 0.2508 - val_accuracy: 0.9690 - lr: 1.0000e-04
Epoch 38/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1823 - accuracy: 0.9918 - val_loss: 0.2365 - val_accuracy: 0.9763 - lr: 1.0000e-04
Epoch 39/100
71/71 [==============================] - 4s 60ms/step - loss: 0.1776 - accuracy: 0.9920 - val_loss: 0.2301 - val_accuracy: 0.9777 - lr: 1.0000e-04
Epoch 40/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1719 - accuracy: 0.9947 - val_loss: 0.2199 - val_accuracy: 0.9797 - lr: 1.0000e-04
Epoch 41/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1724 - accuracy: 0.9938 - val_loss: 0.2247 - val_accuracy: 0.9777 - lr: 1.0000e-04
Epoch 42/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1692 - accuracy: 0.9949 - val_loss: 0.2228 - val_accuracy: 0.9793 - lr: 1.0000e-04
Epoch 43/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1653 - accuracy: 0.9946 - val_loss: 0.2141 - val_accuracy: 0.9803 - lr: 1.0000e-04
Epoch 44/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1662 - accuracy: 0.9946 - val_loss: 0.2149 - val_accuracy: 0.9810 - lr: 1.0000e-04
Epoch 45/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1622 - accuracy: 0.9950 - val_loss: 0.2074 - val_accuracy: 0.9827 - lr: 1.0000e-04
Epoch 46/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1597 - accuracy: 0.9957 - val_loss: 0.2066 - val_accuracy: 0.9820 - lr: 1.0000e-04
Epoch 47/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1599 - accuracy: 0.9961 - val_loss: 0.2058 - val_accuracy: 0.9823 - lr: 1.0000e-04
Epoch 48/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1564 - accuracy: 0.9966 - val_loss: 0.2037 - val_accuracy: 0.9840 - lr: 1.0000e-04
Epoch 49/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1541 - accuracy: 0.9969 - val_loss: 0.2047 - val_accuracy: 0.9817 - lr: 1.0000e-04
Epoch 50/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1518 - accuracy: 0.9975 - val_loss: 0.2042 - val_accuracy: 0.9827 - lr: 1.0000e-04
Epoch 51/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1506 - accuracy: 0.9965 - val_loss: 0.1989 - val_accuracy: 0.9823 - lr: 1.0000e-04
Epoch 52/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1494 - accuracy: 0.9968 - val_loss: 0.1993 - val_accuracy: 0.9810 - lr: 1.0000e-04
Epoch 53/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1476 - accuracy: 0.9976 - val_loss: 0.1991 - val_accuracy: 0.9827 - lr: 1.0000e-04
Epoch 54/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1478 - accuracy: 0.9966 - val_loss: 0.2084 - val_accuracy: 0.9777 - lr: 1.0000e-04
Epoch 55/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1464 - accuracy: 0.9968 - val_loss: 0.2190 - val_accuracy: 0.9747 - lr: 1.0000e-04
Epoch 56/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1429 - accuracy: 0.9976 - val_loss: 0.1886 - val_accuracy: 0.9833 - lr: 1.0000e-04
Epoch 57/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1415 - accuracy: 0.9981 - val_loss: 0.1906 - val_accuracy: 0.9817 - lr: 1.0000e-04
Epoch 58/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1412 - accuracy: 0.9975 - val_loss: 0.1832 - val_accuracy: 0.9843 - lr: 1.0000e-04
Epoch 59/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1388 - accuracy: 0.9976 - val_loss: 0.1984 - val_accuracy: 0.9827 - lr: 1.0000e-04
Epoch 60/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1373 - accuracy: 0.9973 - val_loss: 0.1901 - val_accuracy: 0.9813 - lr: 1.0000e-04
Epoch 61/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1372 - accuracy: 0.9973 - val_loss: 0.1920 - val_accuracy: 0.9823 - lr: 1.0000e-04
Epoch 62/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1344 - accuracy: 0.9980 - val_loss: 0.1873 - val_accuracy: 0.9817 - lr: 1.0000e-04
Epoch 63/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1354 - accuracy: 0.9977 - val_loss: 0.1844 - val_accuracy: 0.9840 - lr: 1.0000e-04
Epoch 64/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1336 - accuracy: 0.9978 - val_loss: 0.1784 - val_accuracy: 0.9843 - lr: 1.0000e-05
Epoch 65/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1312 - accuracy: 0.9984 - val_loss: 0.1783 - val_accuracy: 0.9850 - lr: 1.0000e-05
Epoch 66/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1311 - accuracy: 0.9984 - val_loss: 0.1776 - val_accuracy: 0.9850 - lr: 1.0000e-05
Epoch 67/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1310 - accuracy: 0.9981 - val_loss: 0.1815 - val_accuracy: 0.9840 - lr: 1.0000e-05
Epoch 68/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1303 - accuracy: 0.9981 - val_loss: 0.1792 - val_accuracy: 0.9843 - lr: 1.0000e-05
Epoch 69/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1298 - accuracy: 0.9989 - val_loss: 0.1759 - val_accuracy: 0.9847 - lr: 1.0000e-05
Epoch 70/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1295 - accuracy: 0.9992 - val_loss: 0.1749 - val_accuracy: 0.9863 - lr: 1.0000e-05
Epoch 71/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1289 - accuracy: 0.9993 - val_loss: 0.1749 - val_accuracy: 0.9853 - lr: 1.0000e-05
Epoch 72/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1293 - accuracy: 0.9993 - val_loss: 0.1742 - val_accuracy: 0.9850 - lr: 1.0000e-05
Epoch 73/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1281 - accuracy: 0.9990 - val_loss: 0.1731 - val_accuracy: 0.9853 - lr: 1.0000e-05
Epoch 74/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1298 - accuracy: 0.9988 - val_loss: 0.1752 - val_accuracy: 0.9863 - lr: 1.0000e-05
Epoch 75/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1296 - accuracy: 0.9989 - val_loss: 0.1742 - val_accuracy: 0.9850 - lr: 1.0000e-05
Epoch 76/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1304 - accuracy: 0.9978 - val_loss: 0.1731 - val_accuracy: 0.9853 - lr: 1.0000e-05
Epoch 77/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1294 - accuracy: 0.9990 - val_loss: 0.1757 - val_accuracy: 0.9857 - lr: 1.0000e-05
Epoch 78/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1297 - accuracy: 0.9986 - val_loss: 0.1767 - val_accuracy: 0.9853 - lr: 1.0000e-05
Epoch 79/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1301 - accuracy: 0.9981 - val_loss: 0.1734 - val_accuracy: 0.9853 - lr: 1.0000e-06
Epoch 80/100
71/71 [==============================] - 4s 59ms/step - loss: 0.1275 - accuracy: 0.9991 - val_loss: 0.1752 - val_accuracy: 0.9850 - lr: 1.0000e-06
94/94 [==============================] - 1s 6ms/step
Cohen’s Kappa Score: 0.9832142857142857
for fig in figures_cutout:
fig()
overall.iloc[-3:]
| Model Name | Epochs | Batch Size | Train Loss | Test Loss | Train Acc | Test Acc | Kappa | Comments | |
|---|---|---|---|---|---|---|---|---|---|
| 9 | CNN cutout | 44 | 128 | 0.009736 | 0.181197 | 0.997674 | 0.951667 | 0.956429 | NaN |
| 10 | CNN2 cutout | 45 | 128 | 0.042143 | 0.069994 | 0.994019 | 0.984000 | 0.981786 | NaN |
| 11 | VGG_Baseline cutout | 80 | 128 | 0.129479 | 0.174903 | 0.999225 | 0.986333 | 0.983214 | NaN |
Goal:
def cnn_lr(optimizer='adam', name='CNN_Baseline_LR', l2_reg=0.01):
model = Sequential(name=name)
# Input layer
model.add(Input(shape=(128, 128, 1)))
# First Convolutional Block
model.add(Conv2D(64, (3, 3), padding='same', activation='relu', kernel_regularizer=l2(l2_reg)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
# Second Convolutional Block
model.add(Conv2D(128, (3, 3), padding='same', activation='relu', kernel_regularizer=l2(l2_reg)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
# Third Convolutional Block
model.add(Conv2D(64, (3, 3), padding='same', activation='relu', kernel_regularizer=l2(l2_reg)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.4))
model.add(Flatten())
# Dense Block with L2
model.add(Dense(512, activation='relu', kernel_regularizer=l2(l2_reg)))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu', kernel_regularizer=l2(l2_reg)))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(15, activation='softmax'))
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
return model
cnn_lr = cnn_lr()
Model: "CNN_Baseline_LR"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 128, 128, 64) 640
batch_normalization (BatchN (None, 128, 128, 64) 256
ormalization)
max_pooling2d (MaxPooling2D (None, 64, 64, 64) 0
)
dropout (Dropout) (None, 64, 64, 64) 0
conv2d_1 (Conv2D) (None, 64, 64, 128) 73856
batch_normalization_1 (Batc (None, 64, 64, 128) 512
hNormalization)
max_pooling2d_1 (MaxPooling (None, 32, 32, 128) 0
2D)
dropout_1 (Dropout) (None, 32, 32, 128) 0
conv2d_2 (Conv2D) (None, 32, 32, 64) 73792
batch_normalization_2 (Batc (None, 32, 32, 64) 256
hNormalization)
max_pooling2d_2 (MaxPooling (None, 16, 16, 64) 0
2D)
dropout_2 (Dropout) (None, 16, 16, 64) 0
flatten (Flatten) (None, 16384) 0
dense (Dense) (None, 512) 8389120
batch_normalization_3 (Batc (None, 512) 2048
hNormalization)
dropout_3 (Dropout) (None, 512) 0
dense_1 (Dense) (None, 128) 65664
batch_normalization_4 (Batc (None, 128) 512
hNormalization)
dropout_4 (Dropout) (None, 128) 0
dense_2 (Dense) (None, 15) 1935
=================================================================
Total params: 8,608,591
Trainable params: 8,606,799
Non-trainable params: 1,792
_________________________________________________________________
results_cnn_lr, fig_cnn_lr = evaluator.model_evaluate( train_ds, val_ds , cnn_lr, base_hparams)
Epoch 1/100 71/71 [==============================] - 5s 53ms/step - loss: 14.9827 - accuracy: 0.3425 - val_loss: 14.0496 - val_accuracy: 0.1783 - lr: 0.0010 Epoch 2/100 71/71 [==============================] - 3s 44ms/step - loss: 8.7951 - accuracy: 0.5913 - val_loss: 7.7349 - val_accuracy: 0.3440 - lr: 0.0010 Epoch 3/100 71/71 [==============================] - 3s 44ms/step - loss: 5.9010 - accuracy: 0.6977 - val_loss: 7.0035 - val_accuracy: 0.2513 - lr: 0.0010 Epoch 4/100 71/71 [==============================] - 3s 44ms/step - loss: 4.8807 - accuracy: 0.7591 - val_loss: 5.6895 - val_accuracy: 0.4397 - lr: 0.0010 Epoch 5/100 71/71 [==============================] - 3s 44ms/step - loss: 4.2563 - accuracy: 0.7941 - val_loss: 5.3168 - val_accuracy: 0.5137 - lr: 0.0010 Epoch 6/100 71/71 [==============================] - 3s 44ms/step - loss: 4.0950 - accuracy: 0.8250 - val_loss: 4.9939 - val_accuracy: 0.5263 - lr: 0.0010 Epoch 7/100 71/71 [==============================] - 3s 44ms/step - loss: 3.7052 - accuracy: 0.8537 - val_loss: 8.6494 - val_accuracy: 0.1347 - lr: 0.0010 Epoch 8/100 71/71 [==============================] - 3s 44ms/step - loss: 3.6032 - accuracy: 0.8620 - val_loss: 6.2482 - val_accuracy: 0.2923 - lr: 0.0010 Epoch 9/100 71/71 [==============================] - 3s 44ms/step - loss: 3.5958 - accuracy: 0.8683 - val_loss: 4.6657 - val_accuracy: 0.5677 - lr: 0.0010 Epoch 10/100 71/71 [==============================] - 3s 44ms/step - loss: 3.4977 - accuracy: 0.8749 - val_loss: 4.6597 - val_accuracy: 0.6000 - lr: 0.0010 Epoch 11/100 71/71 [==============================] - 3s 44ms/step - loss: 3.6819 - accuracy: 0.8721 - val_loss: 8.3454 - val_accuracy: 0.3127 - lr: 0.0010 Epoch 12/100 71/71 [==============================] - 3s 44ms/step - loss: 3.6828 - accuracy: 0.8785 - val_loss: 5.5176 - val_accuracy: 0.4833 - lr: 0.0010 Epoch 13/100 71/71 [==============================] - 3s 44ms/step - loss: 3.5147 - accuracy: 0.8906 - val_loss: 5.2196 - val_accuracy: 0.5153 - lr: 0.0010 Epoch 14/100 71/71 [==============================] - 3s 44ms/step - loss: 3.5609 - accuracy: 0.8861 - val_loss: 4.4416 - val_accuracy: 0.7063 - lr: 0.0010 Epoch 15/100 71/71 [==============================] - 3s 44ms/step - loss: 3.7102 - accuracy: 0.8878 - val_loss: 5.7496 - val_accuracy: 0.4623 - lr: 0.0010 Epoch 16/100 71/71 [==============================] - 3s 44ms/step - loss: 3.6359 - accuracy: 0.8918 - val_loss: 4.4195 - val_accuracy: 0.6877 - lr: 0.0010 Epoch 17/100 71/71 [==============================] - 3s 44ms/step - loss: 3.6130 - accuracy: 0.8927 - val_loss: 4.1744 - val_accuracy: 0.6743 - lr: 0.0010 Epoch 18/100 71/71 [==============================] - 3s 44ms/step - loss: 3.4854 - accuracy: 0.8938 - val_loss: 5.4007 - val_accuracy: 0.5643 - lr: 0.0010 Epoch 19/100 71/71 [==============================] - 3s 44ms/step - loss: 3.6936 - accuracy: 0.9025 - val_loss: 4.0829 - val_accuracy: 0.7067 - lr: 0.0010 Epoch 20/100 71/71 [==============================] - 3s 44ms/step - loss: 3.5595 - accuracy: 0.8961 - val_loss: 6.7631 - val_accuracy: 0.3923 - lr: 0.0010 Epoch 21/100 71/71 [==============================] - 3s 44ms/step - loss: 3.3630 - accuracy: 0.9093 - val_loss: 4.8026 - val_accuracy: 0.6080 - lr: 0.0010 Epoch 22/100 71/71 [==============================] - 3s 44ms/step - loss: 3.6045 - accuracy: 0.8942 - val_loss: 4.6349 - val_accuracy: 0.6700 - lr: 0.0010 Epoch 23/100 71/71 [==============================] - 3s 44ms/step - loss: 3.7745 - accuracy: 0.8954 - val_loss: 5.8354 - val_accuracy: 0.5153 - lr: 0.0010 Epoch 24/100 71/71 [==============================] - 3s 44ms/step - loss: 3.4252 - accuracy: 0.9116 - val_loss: 5.2437 - val_accuracy: 0.5150 - lr: 0.0010 Epoch 25/100 71/71 [==============================] - 3s 44ms/step - loss: 3.1529 - accuracy: 0.9425 - val_loss: 3.5722 - val_accuracy: 0.7687 - lr: 1.0000e-04 Epoch 26/100 71/71 [==============================] - 3s 44ms/step - loss: 2.6657 - accuracy: 0.9725 - val_loss: 2.5599 - val_accuracy: 0.9393 - lr: 1.0000e-04 Epoch 27/100 71/71 [==============================] - 3s 44ms/step - loss: 2.2905 - accuracy: 0.9784 - val_loss: 2.2732 - val_accuracy: 0.9280 - lr: 1.0000e-04 Epoch 28/100 71/71 [==============================] - 3s 44ms/step - loss: 1.9705 - accuracy: 0.9847 - val_loss: 1.9093 - val_accuracy: 0.9557 - lr: 1.0000e-04 Epoch 29/100 71/71 [==============================] - 3s 44ms/step - loss: 1.7019 - accuracy: 0.9880 - val_loss: 1.8302 - val_accuracy: 0.9070 - lr: 1.0000e-04 Epoch 30/100 71/71 [==============================] - 3s 44ms/step - loss: 1.4787 - accuracy: 0.9894 - val_loss: 1.4622 - val_accuracy: 0.9610 - lr: 1.0000e-04 Epoch 31/100 71/71 [==============================] - 3s 43ms/step - loss: 1.2851 - accuracy: 0.9912 - val_loss: 1.3713 - val_accuracy: 0.9360 - lr: 1.0000e-04 Epoch 32/100 71/71 [==============================] - 3s 45ms/step - loss: 1.1304 - accuracy: 0.9919 - val_loss: 1.1340 - val_accuracy: 0.9637 - lr: 1.0000e-04 Epoch 33/100 71/71 [==============================] - 3s 44ms/step - loss: 0.9971 - accuracy: 0.9918 - val_loss: 1.1236 - val_accuracy: 0.9317 - lr: 1.0000e-04 Epoch 34/100 71/71 [==============================] - 3s 44ms/step - loss: 0.8820 - accuracy: 0.9951 - val_loss: 0.9215 - val_accuracy: 0.9647 - lr: 1.0000e-04 Epoch 35/100 71/71 [==============================] - 3s 44ms/step - loss: 0.7901 - accuracy: 0.9944 - val_loss: 0.8854 - val_accuracy: 0.9493 - lr: 1.0000e-04 Epoch 36/100 71/71 [==============================] - 3s 44ms/step - loss: 0.7138 - accuracy: 0.9948 - val_loss: 0.7876 - val_accuracy: 0.9593 - lr: 1.0000e-04 Epoch 37/100 71/71 [==============================] - 3s 44ms/step - loss: 0.6501 - accuracy: 0.9950 - val_loss: 0.7119 - val_accuracy: 0.9637 - lr: 1.0000e-04 Epoch 38/100 71/71 [==============================] - 3s 44ms/step - loss: 0.6037 - accuracy: 0.9950 - val_loss: 0.6778 - val_accuracy: 0.9643 - lr: 1.0000e-04 Epoch 39/100 71/71 [==============================] - 3s 44ms/step - loss: 0.5697 - accuracy: 0.9947 - val_loss: 0.6692 - val_accuracy: 0.9560 - lr: 1.0000e-04 Epoch 40/100 71/71 [==============================] - 3s 44ms/step - loss: 0.5531 - accuracy: 0.9924 - val_loss: 0.9872 - val_accuracy: 0.8650 - lr: 1.0000e-04 Epoch 41/100 71/71 [==============================] - 3s 44ms/step - loss: 0.5371 - accuracy: 0.9930 - val_loss: 0.7393 - val_accuracy: 0.9267 - lr: 1.0000e-04 Epoch 42/100 71/71 [==============================] - 3s 44ms/step - loss: 0.5282 - accuracy: 0.9929 - val_loss: 0.6406 - val_accuracy: 0.9520 - lr: 1.0000e-04 Epoch 43/100 71/71 [==============================] - 3s 44ms/step - loss: 0.5256 - accuracy: 0.9906 - val_loss: 0.6383 - val_accuracy: 0.9567 - lr: 1.0000e-04 Epoch 44/100 71/71 [==============================] - 3s 44ms/step - loss: 0.5316 - accuracy: 0.9894 - val_loss: 0.6111 - val_accuracy: 0.9600 - lr: 1.0000e-04
fig_cnn_lr()
pd.Series(results_cnn_lr)
Model Name CNN_Baseline_LR Epochs 44 Batch Size 128 Train Loss 0.881994 Test Loss 0.921476 Train Acc 0.995126 Test Acc 0.964667 dtype: object
# pd.Series(results_cnn)
cls_df_cnn_lr= evaluation_test(cnn_lr, X_test_big, y_test_big, labels_dict)
cls_df_cnn_lr
94/94 [==============================] - 1s 4ms/step Accuracy: 0.967
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| Potato | 0.926108 | 0.940 | 0.933002 | 200.000 |
| Papaya | 0.915094 | 0.970 | 0.941748 | 200.000 |
| Carrot | 0.968421 | 0.920 | 0.943590 | 200.000 |
| Brinjal | 0.950739 | 0.965 | 0.957816 | 200.000 |
| Cabbage | 0.929577 | 0.990 | 0.958838 | 200.000 |
| Capsicum | 0.942584 | 0.985 | 0.963325 | 200.000 |
| Radish | 0.989529 | 0.945 | 0.966752 | 200.000 |
| accuracy | 0.967000 | 0.967 | 0.967000 | 0.967 |
| macro avg | 0.967798 | 0.967 | 0.967094 | 3000.000 |
| weighted avg | 0.967798 | 0.967 | 0.967094 | 3000.000 |
| Cauliflower | 0.989637 | 0.955 | 0.972010 | 200.000 |
| Cucumber | 0.979695 | 0.965 | 0.972292 | 200.000 |
| Tomato | 0.970297 | 0.980 | 0.975124 | 200.000 |
| Pumpkin | 0.989796 | 0.970 | 0.979798 | 200.000 |
| Broccoli | 1.000000 | 0.965 | 0.982188 | 200.000 |
| Bottle_Gourd | 1.000000 | 0.970 | 0.984772 | 200.000 |
| Bean | 0.975490 | 0.995 | 0.985149 | 200.000 |
| Bitter_Gourd | 0.990000 | 0.990 | 0.990000 | 200.000 |
plot_classification_heatmap(cls_df_cnn_lr)
y_pred = cnn_lr.predict(X_test_big)
y_pred_classes = np.argmax(y_pred, axis=1)
kappa_cnn_lr = cohen_kappa_score(y_test_big, y_pred_classes)
print("Cohen’s Kappa Score:", kappa_cnn_lr)
94/94 [==============================] - 0s 4ms/step Cohen’s Kappa Score: 0.9646428571428571
results_cnn_lr['Model Name'] = 'CNN_Reg'
results_cnn_lr['Kappa'] = kappa_cnn_lr
overall = pd.concat([overall, pd.DataFrame([results_cnn_lr])], ignore_index=True)
overall
| Model Name | Epochs | Batch Size | Train Loss | Test Loss | Train Acc | Test Acc | Kappa | Comments | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | CNN basic one | 38 | 128 | 0.017855 | 0.623409 | 0.996677 | 0.840000 | 0.839286 | NaN |
| 1 | CNN2 basic one | 53 | 128 | 0.015596 | 0.062343 | 0.999668 | 0.982667 | 0.985000 | NaN |
| 2 | VGG_Baseline basic one | 63 | 128 | 0.169651 | 0.240200 | 0.996788 | 0.974000 | 0.972500 | NaN |
| 3 | CNN basic two | 24 | 128 | 0.228279 | 0.786181 | 0.941294 | 0.758333 | 0.745714 | NaN |
| 4 | CNN2 basic two | 77 | 128 | 0.038791 | 0.089882 | 0.997009 | 0.979000 | 0.979286 | NaN |
| 5 | VGG_Baseline basic two | 98 | 128 | 0.144072 | 0.220837 | 0.999778 | 0.976333 | 0.974643 | NaN |
| 6 | CNN cutmix | 42 | 128 | 0.164451 | 0.178881 | 0.998892 | 0.945667 | 0.951071 | NaN |
| 7 | CNN2 cutmix | 52 | 128 | 0.157783 | 0.055629 | 0.999335 | 0.992333 | 0.990714 | NaN |
| 8 | VGG_Baseline cutmix | 71 | 128 | 0.238155 | 0.141491 | 0.999889 | 0.994333 | 0.991429 | NaN |
| 9 | CNN cutout | 44 | 128 | 0.009736 | 0.181197 | 0.997674 | 0.951667 | 0.956429 | NaN |
| 10 | CNN2 cutout | 45 | 128 | 0.042143 | 0.069994 | 0.994019 | 0.984000 | 0.981786 | NaN |
| 11 | VGG_Baseline cutout | 80 | 128 | 0.129479 | 0.174903 | 0.999225 | 0.986333 | 0.983214 | NaN |
| 12 | CNN_Reg | 44 | 128 | 0.881994 | 0.921476 | 0.995126 | 0.964667 | 0.964643 | NaN |
def CNN2_Reg(name='CNN2_Baseline', l2_reg=0.01, dropout_rate=0.5):
filters_list = [32, 64, 128, 128, 128, 128]
model = Sequential(name=name)
model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same', input_shape=(128, 128, 1), kernel_regularizer=l2(l2_reg)))
model.add(BatchNormalization())
model.add(ReLU())
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same', kernel_regularizer=l2(l2_reg)))
model.add(BatchNormalization())
model.add(ReLU())
model.add(Dropout(dropout_rate))
for filters in filters_list[2:]:
model.add(Conv2D(filters=filters, kernel_size=(3, 3), padding='same', kernel_regularizer=l2(l2_reg)))
model.add(BatchNormalization())
model.add(ReLU())
model.add(GlobalAveragePooling2D())
model.add(Dense(256, activation='relu', kernel_regularizer=l2(l2_reg)))
model.add(Dropout(dropout_rate))
model.add(Dense(15, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
return model
cnn2_reg = CNN2_Reg()
Model: "CNN2_Baseline"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 128, 128, 32) 320
batch_normalization (BatchN (None, 128, 128, 32) 128
ormalization)
re_lu (ReLU) (None, 128, 128, 32) 0
conv2d_1 (Conv2D) (None, 128, 128, 64) 18496
batch_normalization_1 (Batc (None, 128, 128, 64) 256
hNormalization)
re_lu_1 (ReLU) (None, 128, 128, 64) 0
dropout (Dropout) (None, 128, 128, 64) 0
conv2d_2 (Conv2D) (None, 128, 128, 128) 73856
batch_normalization_2 (Batc (None, 128, 128, 128) 512
hNormalization)
re_lu_2 (ReLU) (None, 128, 128, 128) 0
conv2d_3 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_3 (Batc (None, 128, 128, 128) 512
hNormalization)
re_lu_3 (ReLU) (None, 128, 128, 128) 0
conv2d_4 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_4 (Batc (None, 128, 128, 128) 512
hNormalization)
re_lu_4 (ReLU) (None, 128, 128, 128) 0
conv2d_5 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_5 (Batc (None, 128, 128, 128) 512
hNormalization)
re_lu_5 (ReLU) (None, 128, 128, 128) 0
global_average_pooling2d (G (None, 128) 0
lobalAveragePooling2D)
dense (Dense) (None, 256) 33024
dropout_1 (Dropout) (None, 256) 0
dense_1 (Dense) (None, 15) 3855
=================================================================
Total params: 574,735
Trainable params: 573,519
Non-trainable params: 1,216
_________________________________________________________________
results_cnn2_lr, fig_cnn2_lr = evaluator.model_evaluate( train_ds_cutmix, val_ds_cutmix , cnn2_reg, base_hparams)
Epoch 1/100 71/71 [==============================] - 19s 262ms/step - loss: 0.4528 - accuracy: 0.9846 - val_loss: 0.3354 - val_accuracy: 0.9693 - lr: 1.0000e-07 Epoch 2/100 71/71 [==============================] - 19s 261ms/step - loss: 0.4495 - accuracy: 0.9870 - val_loss: 0.3407 - val_accuracy: 0.9663 - lr: 1.0000e-07 Epoch 3/100 71/71 [==============================] - 19s 261ms/step - loss: 0.4488 - accuracy: 0.9870 - val_loss: 0.3448 - val_accuracy: 0.9640 - lr: 1.0000e-07 Epoch 4/100 71/71 [==============================] - 19s 262ms/step - loss: 0.4494 - accuracy: 0.9870 - val_loss: 0.3489 - val_accuracy: 0.9623 - lr: 1.0000e-07 Epoch 5/100 71/71 [==============================] - 19s 261ms/step - loss: 0.4510 - accuracy: 0.9873 - val_loss: 0.3508 - val_accuracy: 0.9600 - lr: 1.0000e-07 Epoch 6/100 71/71 [==============================] - 19s 261ms/step - loss: 0.4502 - accuracy: 0.9872 - val_loss: 0.3522 - val_accuracy: 0.9590 - lr: 1.0000e-07 Epoch 7/100 71/71 [==============================] - 19s 261ms/step - loss: 0.4488 - accuracy: 0.9852 - val_loss: 0.3514 - val_accuracy: 0.9593 - lr: 1.0000e-08 Epoch 8/100 71/71 [==============================] - 19s 262ms/step - loss: 0.4497 - accuracy: 0.9862 - val_loss: 0.3506 - val_accuracy: 0.9593 - lr: 1.0000e-08 Epoch 9/100 71/71 [==============================] - 19s 262ms/step - loss: 0.4514 - accuracy: 0.9858 - val_loss: 0.3507 - val_accuracy: 0.9593 - lr: 1.0000e-08 Epoch 10/100 71/71 [==============================] - 19s 262ms/step - loss: 0.4511 - accuracy: 0.9853 - val_loss: 0.3511 - val_accuracy: 0.9593 - lr: 1.0000e-08 Epoch 11/100 71/71 [==============================] - 19s 262ms/step - loss: 0.4522 - accuracy: 0.9855 - val_loss: 0.3505 - val_accuracy: 0.9593 - lr: 1.0000e-08
pd.Series(results_cnn2_lr)
Model Name CNN2_Baseline Epochs 78 Batch Size 128 Train Loss 0.307886 Test Loss 0.334669 Train Acc 0.985711 Test Acc 0.970333 dtype: object
# pd.Series(results_cnn2)
fig_cnn2_lr()
cls_df_cnn2lr= evaluation_test(cnn2_reg, X_test_big, y_test_big, labels_dict)
cls_df_cnn2lr
94/94 [==============================] - 2s 16ms/step Accuracy: 0.9693333333333334
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| Cauliflower | 0.903670 | 0.985000 | 0.942584 | 200.000000 |
| Potato | 0.949749 | 0.945000 | 0.947368 | 200.000000 |
| Tomato | 0.904977 | 1.000000 | 0.950119 | 200.000000 |
| Radish | 0.994536 | 0.910000 | 0.950392 | 200.000000 |
| Papaya | 0.924883 | 0.985000 | 0.953995 | 200.000000 |
| Pumpkin | 0.960591 | 0.975000 | 0.967742 | 200.000000 |
| accuracy | 0.969333 | 0.969333 | 0.969333 | 0.969333 |
| weighted avg | 0.970798 | 0.969333 | 0.969509 | 3000.000000 |
| macro avg | 0.970798 | 0.969333 | 0.969509 | 3000.000000 |
| Cucumber | 0.989637 | 0.955000 | 0.972010 | 200.000000 |
| Bitter_Gourd | 0.984615 | 0.960000 | 0.972152 | 200.000000 |
| Bean | 0.989744 | 0.965000 | 0.977215 | 200.000000 |
| Capsicum | 0.994845 | 0.965000 | 0.979695 | 200.000000 |
| Broccoli | 0.989796 | 0.970000 | 0.979798 | 200.000000 |
| Cabbage | 0.994872 | 0.970000 | 0.982278 | 200.000000 |
| Brinjal | 1.000000 | 0.970000 | 0.984772 | 200.000000 |
| Carrot | 0.990000 | 0.990000 | 0.990000 | 200.000000 |
| Bottle_Gourd | 0.990050 | 0.995000 | 0.992519 | 200.000000 |
plot_classification_heatmap(cls_df_cnn2lr)
y_pred = cnn2_reg.predict(X_test_big)
y_pred_classes = np.argmax(y_pred, axis=1)
kappa_cnn2_reg = cohen_kappa_score(y_test_big, y_pred_classes)
print("Cohen’s Kappa Score:", kappa_cnn2_reg)
94/94 [==============================] - 2s 16ms/step Cohen’s Kappa Score: 0.9671428571428572
results_cnn2_lr['Model Name'] = 'CNN2_Reg'
results_cnn2_lr['Kappa'] = kappa_cnn2_reg
overall = pd.concat([overall, pd.DataFrame([results_cnn2_lr])], ignore_index=True)
overall
| Model Name | Epochs | Batch Size | Train Loss | Test Loss | Train Acc | Test Acc | Kappa | Comments | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | CNN basic one | 38 | 128 | 0.017855 | 0.623409 | 0.996677 | 0.840000 | 0.839286 | NaN |
| 1 | CNN2 basic one | 53 | 128 | 0.015596 | 0.062343 | 0.999668 | 0.982667 | 0.985000 | NaN |
| 2 | VGG_Baseline basic one | 63 | 128 | 0.169651 | 0.240200 | 0.996788 | 0.974000 | 0.972500 | NaN |
| 3 | CNN basic two | 24 | 128 | 0.228279 | 0.786181 | 0.941294 | 0.758333 | 0.745714 | NaN |
| 4 | CNN2 basic two | 77 | 128 | 0.038791 | 0.089882 | 0.997009 | 0.979000 | 0.979286 | NaN |
| 5 | VGG_Baseline basic two | 98 | 128 | 0.144072 | 0.220837 | 0.999778 | 0.976333 | 0.974643 | NaN |
| 6 | CNN cutmix | 42 | 128 | 0.164451 | 0.178881 | 0.998892 | 0.945667 | 0.951071 | NaN |
| 7 | CNN2 cutmix | 52 | 128 | 0.157783 | 0.055629 | 0.999335 | 0.992333 | 0.990714 | NaN |
| 8 | VGG_Baseline cutmix | 71 | 128 | 0.238155 | 0.141491 | 0.999889 | 0.994333 | 0.991429 | NaN |
| 9 | CNN cutout | 44 | 128 | 0.009736 | 0.181197 | 0.997674 | 0.951667 | 0.956429 | NaN |
| 10 | CNN2 cutout | 45 | 128 | 0.042143 | 0.069994 | 0.994019 | 0.984000 | 0.981786 | NaN |
| 11 | VGG_Baseline cutout | 80 | 128 | 0.129479 | 0.174903 | 0.999225 | 0.986333 | 0.983214 | NaN |
| 12 | CNN_Reg | 44 | 128 | 0.881994 | 0.921476 | 0.995126 | 0.964667 | 0.964643 | NaN |
| 13 | CNN2_Reg | 78 | 128 | 0.307886 | 0.334669 | 0.985711 | 0.970333 | 0.967143 | NaN |
Data used will be the original dataset
def lr_warmup_cosine_decay(global_step=10, warmup_steps=100, hold=0, total_steps=1000, start_lr=0.01, target_lr=1e-3):
if total_steps <= warmup_steps:
raise ValueError("total_steps must be greater than warmup_steps.")
global_step = float(global_step)
warmup_steps = float(warmup_steps)
hold = float(hold)
total_steps = float(total_steps)
start_lr = float(start_lr)
target_lr = float(target_lr)
if global_step < warmup_steps:
warmup_lr = start_lr + (target_lr - start_lr) * (global_step / warmup_steps)
return warmup_lr
elif global_step <= warmup_steps + hold:
return target_lr
else:
decayed = 0.5 * (1 + np.cos(np.pi * (global_step - warmup_steps - hold) / (total_steps - warmup_steps - hold)))
decayed_lr = target_lr + (start_lr - target_lr) * decayed
return decayed_lr
def CNN1_reg(optimizer=None, name='CNN_secial_R'):
model = Sequential(name=name)
# Input layer
model.add(Input(shape=(128, 128, 1)))
# First Convolutional Block
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
# Second Convolutional Block
model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
# Third Convolutional Block
model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.4))
model.add(Flatten())
# Dense Block
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
# Dense Block
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
# Output Layer
model.add(Dense(15, activation='softmax'))
# Compile the model
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
return model
cnn_reg = CNN1_reg(optimizer = optimizer)
lr_schedule = tf.keras.callbacks.LearningRateScheduler(lr_warmup_cosine_decay)
initial_lr = 0.001
optimizer = tf.keras.optimizers.Adam(learning_rate=initial_lr)
results_cnn_clr, fig_cnn_clr = evaluator.model_evaluate( train_ds_cutmix, val_ds_cutmix , cnn_reg , base_hparams,callbacks=[EarlyStopping(monitor='accuracy', patience=10, restore_best_weights=True), ReduceLROnPlateau(patience=5), lr_schedule])
Model: "CNN_secial_R"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 128, 128, 64) 640
batch_normalization (BatchN (None, 128, 128, 64) 256
ormalization)
max_pooling2d (MaxPooling2D (None, 64, 64, 64) 0
)
dropout (Dropout) (None, 64, 64, 64) 0
conv2d_1 (Conv2D) (None, 64, 64, 128) 73856
batch_normalization_1 (Batc (None, 64, 64, 128) 512
hNormalization)
max_pooling2d_1 (MaxPooling (None, 32, 32, 128) 0
2D)
dropout_1 (Dropout) (None, 32, 32, 128) 0
conv2d_2 (Conv2D) (None, 32, 32, 256) 295168
batch_normalization_2 (Batc (None, 32, 32, 256) 1024
hNormalization)
max_pooling2d_2 (MaxPooling (None, 16, 16, 256) 0
2D)
dropout_2 (Dropout) (None, 16, 16, 256) 0
flatten (Flatten) (None, 65536) 0
dense (Dense) (None, 512) 33554944
batch_normalization_3 (Batc (None, 512) 2048
hNormalization)
dropout_3 (Dropout) (None, 512) 0
dense_1 (Dense) (None, 128) 65664
batch_normalization_4 (Batc (None, 128) 512
hNormalization)
dropout_4 (Dropout) (None, 128) 0
dense_2 (Dense) (None, 15) 1935
=================================================================
Total params: 33,996,559
Trainable params: 33,994,383
Non-trainable params: 2,176
_________________________________________________________________
Epoch 1/100
71/71 [==============================] - 8s 67ms/step - loss: 2.1403 - accuracy: 0.3684 - val_loss: 35.7514 - val_accuracy: 0.1050 - lr: 0.0100
Epoch 2/100
71/71 [==============================] - 4s 49ms/step - loss: 1.3178 - accuracy: 0.6083 - val_loss: 13.5476 - val_accuracy: 0.0680 - lr: 0.0100
Epoch 3/100
71/71 [==============================] - 4s 50ms/step - loss: 0.9948 - accuracy: 0.7201 - val_loss: 6.1567 - val_accuracy: 0.1627 - lr: 0.0100
Epoch 4/100
71/71 [==============================] - 4s 49ms/step - loss: 0.7657 - accuracy: 0.7935 - val_loss: 7.4167 - val_accuracy: 0.1263 - lr: 0.0100
Epoch 5/100
71/71 [==============================] - 4s 50ms/step - loss: 0.6364 - accuracy: 0.8442 - val_loss: 6.2521 - val_accuracy: 0.1807 - lr: 0.0100
Epoch 6/100
71/71 [==============================] - 4s 49ms/step - loss: 0.5169 - accuracy: 0.8789 - val_loss: 4.7434 - val_accuracy: 0.2987 - lr: 0.0100
Epoch 7/100
71/71 [==============================] - 4s 50ms/step - loss: 0.4263 - accuracy: 0.9114 - val_loss: 1.5271 - val_accuracy: 0.5763 - lr: 0.0100
Epoch 8/100
71/71 [==============================] - 3s 49ms/step - loss: 0.3599 - accuracy: 0.9374 - val_loss: 7.2530 - val_accuracy: 0.1957 - lr: 0.0100
Epoch 9/100
71/71 [==============================] - 4s 49ms/step - loss: 0.3537 - accuracy: 0.9387 - val_loss: 4.0312 - val_accuracy: 0.2790 - lr: 0.0100
Epoch 10/100
71/71 [==============================] - 3s 48ms/step - loss: 0.3693 - accuracy: 0.9321 - val_loss: 13.2840 - val_accuracy: 0.1053 - lr: 0.0100
Epoch 11/100
71/71 [==============================] - 3s 48ms/step - loss: 0.3824 - accuracy: 0.9267 - val_loss: 9.1653 - val_accuracy: 0.1940 - lr: 0.0100
Epoch 12/100
71/71 [==============================] - 4s 49ms/step - loss: 0.3064 - accuracy: 0.9568 - val_loss: 6.8278 - val_accuracy: 0.1520 - lr: 9.9973e-04
Epoch 13/100
71/71 [==============================] - 4s 49ms/step - loss: 0.2629 - accuracy: 0.9711 - val_loss: 2.7199 - val_accuracy: 0.4080 - lr: 0.0100
Epoch 14/100
71/71 [==============================] - 4s 49ms/step - loss: 0.2435 - accuracy: 0.9790 - val_loss: 1.9759 - val_accuracy: 0.4857 - lr: 0.0100
Epoch 15/100
71/71 [==============================] - 4s 49ms/step - loss: 0.2354 - accuracy: 0.9798 - val_loss: 0.4452 - val_accuracy: 0.8697 - lr: 0.0100
Epoch 16/100
71/71 [==============================] - 4s 49ms/step - loss: 0.2270 - accuracy: 0.9817 - val_loss: 1.3683 - val_accuracy: 0.6100 - lr: 0.0100
Epoch 17/100
71/71 [==============================] - 4s 49ms/step - loss: 0.2120 - accuracy: 0.9868 - val_loss: 0.4104 - val_accuracy: 0.8720 - lr: 0.0100
Epoch 18/100
71/71 [==============================] - 4s 49ms/step - loss: 0.2093 - accuracy: 0.9878 - val_loss: 0.6228 - val_accuracy: 0.8217 - lr: 0.0100
Epoch 19/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1987 - accuracy: 0.9909 - val_loss: 0.5716 - val_accuracy: 0.8360 - lr: 0.0100
Epoch 20/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1926 - accuracy: 0.9938 - val_loss: 0.2338 - val_accuracy: 0.9330 - lr: 0.0100
Epoch 21/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1894 - accuracy: 0.9936 - val_loss: 0.6518 - val_accuracy: 0.8123 - lr: 0.0100
Epoch 22/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1807 - accuracy: 0.9957 - val_loss: 0.2763 - val_accuracy: 0.9243 - lr: 0.0100
Epoch 23/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1790 - accuracy: 0.9952 - val_loss: 0.2352 - val_accuracy: 0.9270 - lr: 0.0100
Epoch 24/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1759 - accuracy: 0.9968 - val_loss: 5.4875 - val_accuracy: 0.1527 - lr: 0.0100
Epoch 25/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1768 - accuracy: 0.9961 - val_loss: 0.7944 - val_accuracy: 0.7880 - lr: 9.9872e-04
Epoch 26/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1720 - accuracy: 0.9978 - val_loss: 0.2200 - val_accuracy: 0.9310 - lr: 0.0100
Epoch 27/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1684 - accuracy: 0.9979 - val_loss: 0.8074 - val_accuracy: 0.7620 - lr: 0.0100
Epoch 28/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1709 - accuracy: 0.9969 - val_loss: 0.2422 - val_accuracy: 0.9297 - lr: 0.0100
Epoch 29/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1665 - accuracy: 0.9973 - val_loss: 0.6504 - val_accuracy: 0.8090 - lr: 0.0100
Epoch 30/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1686 - accuracy: 0.9978 - val_loss: 0.2757 - val_accuracy: 0.9220 - lr: 0.0100
Epoch 31/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1654 - accuracy: 0.9983 - val_loss: 0.2051 - val_accuracy: 0.9383 - lr: 0.0100
Epoch 32/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1856 - accuracy: 0.9925 - val_loss: 2.6279 - val_accuracy: 0.3697 - lr: 0.0100
Epoch 33/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1747 - accuracy: 0.9960 - val_loss: 0.9447 - val_accuracy: 0.7333 - lr: 0.0100
Epoch 34/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1669 - accuracy: 0.9982 - val_loss: 0.2551 - val_accuracy: 0.9240 - lr: 0.0100
Epoch 35/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1630 - accuracy: 0.9981 - val_loss: 1.5174 - val_accuracy: 0.5963 - lr: 0.0100
Epoch 36/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1625 - accuracy: 0.9984 - val_loss: 0.2485 - val_accuracy: 0.9257 - lr: 9.9728e-04
Epoch 37/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1621 - accuracy: 0.9980 - val_loss: 0.9798 - val_accuracy: 0.7190 - lr: 0.0100
Epoch 38/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1585 - accuracy: 0.9994 - val_loss: 1.8865 - val_accuracy: 0.5320 - lr: 0.0100
Epoch 39/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1601 - accuracy: 0.9984 - val_loss: 0.2458 - val_accuracy: 0.9317 - lr: 0.0100
Epoch 40/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1578 - accuracy: 0.9990 - val_loss: 0.2024 - val_accuracy: 0.9427 - lr: 0.0100
Epoch 41/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1588 - accuracy: 0.9989 - val_loss: 0.3869 - val_accuracy: 0.8937 - lr: 0.0100
Epoch 42/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1557 - accuracy: 0.9993 - val_loss: 1.4322 - val_accuracy: 0.6083 - lr: 0.0100
Epoch 43/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1567 - accuracy: 0.9997 - val_loss: 0.3280 - val_accuracy: 0.9013 - lr: 0.0100
Epoch 44/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1532 - accuracy: 0.9992 - val_loss: 0.1957 - val_accuracy: 0.9447 - lr: 0.0100
Epoch 45/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1545 - accuracy: 0.9991 - val_loss: 1.5012 - val_accuracy: 0.5937 - lr: 0.0100
Epoch 46/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1519 - accuracy: 0.9994 - val_loss: 0.3777 - val_accuracy: 0.8927 - lr: 0.0100
Epoch 47/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1575 - accuracy: 0.9983 - val_loss: 2.9380 - val_accuracy: 0.3270 - lr: 0.0100
Epoch 48/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1544 - accuracy: 0.9990 - val_loss: 4.4248 - val_accuracy: 0.2030 - lr: 0.0100
Epoch 49/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1570 - accuracy: 0.9984 - val_loss: 0.3463 - val_accuracy: 0.8990 - lr: 9.9490e-04
Epoch 50/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1552 - accuracy: 0.9993 - val_loss: 0.2370 - val_accuracy: 0.9307 - lr: 0.0099
Epoch 51/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1518 - accuracy: 0.9996 - val_loss: 0.2609 - val_accuracy: 0.9243 - lr: 0.0099
Epoch 52/100
71/71 [==============================] - 3s 48ms/step - loss: 0.1513 - accuracy: 0.9993 - val_loss: 1.3782 - val_accuracy: 0.6037 - lr: 0.0099
Epoch 53/100
71/71 [==============================] - 4s 49ms/step - loss: 0.1497 - accuracy: 0.9997 - val_loss: 0.1967 - val_accuracy: 0.9427 - lr: 0.0099
fig_cnn_clr()
pd.Series(results_cnn_clr)
Model Name CNN_secial_R Epochs 53 Batch Size 128 Train Loss 0.153164 Test Loss 0.19572 Train Acc 0.999225 Test Acc 0.944667 dtype: object
def CNN2_Reg(name='CNN2_Reg', dropout_rate=0.5, optimizer = None):
filters_list = [32, 64, 128, 128, 128, 128]
model = Sequential(name=name)
model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same', input_shape=(128, 128, 1)))
model.add(BatchNormalization())
model.add(ReLU())
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same'))
model.add(BatchNormalization())
model.add(ReLU())
model.add(Dropout(dropout_rate))
for filters in filters_list[2:]:
model.add(Conv2D(filters=filters, kernel_size=(3, 3), padding='same'))
model.add(BatchNormalization())
model.add(ReLU())
model.add(GlobalAveragePooling2D())
model.add(Dense(256, activation='relu'))
model.add(Dropout(dropout_rate))
model.add(Dense(15, activation='softmax'))
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
return model
cnn2_reg = CNN2_Reg(optimizer = optimizer)
lr_schedule = tf.keras.callbacks.LearningRateScheduler(lr_warmup_cosine_decay)
initial_lr = 0.001
optimizer = tf.keras.optimizers.Adam(learning_rate=initial_lr)
results_cnn2_clr, fig_cnn2_clr = evaluator.model_evaluate( train_ds_cutmix, val_ds_cutmix , cnn2_reg , base_hparams,callbacks=[EarlyStopping(monitor='accuracy', patience=10, restore_best_weights=True), ReduceLROnPlateau(patience=5), lr_schedule])
Model: "CNN2_Reg"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_17 (Conv2D) (None, 128, 128, 32) 320
batch_normalization_17 (Bat (None, 128, 128, 32) 128
chNormalization)
re_lu_17 (ReLU) (None, 128, 128, 32) 0
conv2d_18 (Conv2D) (None, 128, 128, 64) 18496
batch_normalization_18 (Bat (None, 128, 128, 64) 256
chNormalization)
re_lu_18 (ReLU) (None, 128, 128, 64) 0
dropout_3 (Dropout) (None, 128, 128, 64) 0
conv2d_19 (Conv2D) (None, 128, 128, 128) 73856
batch_normalization_19 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_19 (ReLU) (None, 128, 128, 128) 0
conv2d_20 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_20 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_20 (ReLU) (None, 128, 128, 128) 0
conv2d_21 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_21 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_21 (ReLU) (None, 128, 128, 128) 0
conv2d_22 (Conv2D) (None, 128, 128, 128) 147584
batch_normalization_22 (Bat (None, 128, 128, 128) 512
chNormalization)
re_lu_22 (ReLU) (None, 128, 128, 128) 0
global_average_pooling2d_2 (None, 128) 0
(GlobalAveragePooling2D)
dense_3 (Dense) (None, 256) 33024
dropout_4 (Dropout) (None, 256) 0
dense_4 (Dense) (None, 15) 3855
=================================================================
Total params: 574,735
Trainable params: 573,519
Non-trainable params: 1,216
_________________________________________________________________
Epoch 1/100
71/71 [==============================] - 26s 316ms/step - loss: 2.3510 - accuracy: 0.2343 - val_loss: 74.9609 - val_accuracy: 0.0573 - lr: 0.0100
Epoch 2/100
71/71 [==============================] - 21s 297ms/step - loss: 1.9240 - accuracy: 0.3798 - val_loss: 7.0757 - val_accuracy: 0.1150 - lr: 0.0100
Epoch 3/100
71/71 [==============================] - 21s 297ms/step - loss: 1.7514 - accuracy: 0.4323 - val_loss: 7.7487 - val_accuracy: 0.0787 - lr: 0.0100
Epoch 4/100
71/71 [==============================] - 21s 297ms/step - loss: 1.6588 - accuracy: 0.4713 - val_loss: 29.4657 - val_accuracy: 0.0687 - lr: 0.0100
Epoch 5/100
71/71 [==============================] - 21s 298ms/step - loss: 1.5614 - accuracy: 0.5099 - val_loss: 12.9968 - val_accuracy: 0.1213 - lr: 0.0100
Epoch 6/100
71/71 [==============================] - 21s 297ms/step - loss: 1.4831 - accuracy: 0.5410 - val_loss: 4.7833 - val_accuracy: 0.1963 - lr: 0.0100
Epoch 7/100
71/71 [==============================] - 21s 298ms/step - loss: 1.4217 - accuracy: 0.5589 - val_loss: 21.7436 - val_accuracy: 0.0867 - lr: 0.0100
Epoch 8/100
71/71 [==============================] - 21s 298ms/step - loss: 1.3414 - accuracy: 0.5908 - val_loss: 2.3413 - val_accuracy: 0.3403 - lr: 0.0100
Epoch 9/100
71/71 [==============================] - 21s 298ms/step - loss: 1.2897 - accuracy: 0.6107 - val_loss: 45.9302 - val_accuracy: 0.0690 - lr: 0.0100
Epoch 10/100
71/71 [==============================] - 21s 298ms/step - loss: 1.2588 - accuracy: 0.6206 - val_loss: 4.4542 - val_accuracy: 0.1240 - lr: 0.0100
Epoch 11/100
71/71 [==============================] - 21s 298ms/step - loss: 1.1826 - accuracy: 0.6515 - val_loss: 2.5817 - val_accuracy: 0.3460 - lr: 0.0100
Epoch 12/100
71/71 [==============================] - 21s 298ms/step - loss: 1.1467 - accuracy: 0.6602 - val_loss: 12.7749 - val_accuracy: 0.1857 - lr: 0.0100
Epoch 13/100
71/71 [==============================] - 21s 299ms/step - loss: 1.1121 - accuracy: 0.6739 - val_loss: 7.7926 - val_accuracy: 0.3157 - lr: 9.9968e-04
Epoch 14/100
71/71 [==============================] - 21s 298ms/step - loss: 1.0367 - accuracy: 0.7005 - val_loss: 2.1474 - val_accuracy: 0.4650 - lr: 0.0100
Epoch 15/100
71/71 [==============================] - 21s 298ms/step - loss: 1.0161 - accuracy: 0.7103 - val_loss: 20.6702 - val_accuracy: 0.1043 - lr: 0.0100
Epoch 16/100
71/71 [==============================] - 21s 298ms/step - loss: 0.9726 - accuracy: 0.7273 - val_loss: 1.7594 - val_accuracy: 0.4747 - lr: 0.0100
Epoch 17/100
71/71 [==============================] - 21s 298ms/step - loss: 0.9173 - accuracy: 0.7403 - val_loss: 3.4315 - val_accuracy: 0.3623 - lr: 0.0100
Epoch 18/100
71/71 [==============================] - 21s 298ms/step - loss: 0.9086 - accuracy: 0.7468 - val_loss: 3.3259 - val_accuracy: 0.3013 - lr: 0.0100
Epoch 19/100
71/71 [==============================] - 21s 298ms/step - loss: 0.8940 - accuracy: 0.7523 - val_loss: 19.2103 - val_accuracy: 0.0710 - lr: 0.0100
Epoch 20/100
71/71 [==============================] - 21s 299ms/step - loss: 0.8507 - accuracy: 0.7692 - val_loss: 4.3068 - val_accuracy: 0.3227 - lr: 0.0100
Epoch 21/100
71/71 [==============================] - 21s 298ms/step - loss: 0.8126 - accuracy: 0.7815 - val_loss: 37.8832 - val_accuracy: 0.0953 - lr: 9.9911e-04
Epoch 22/100
71/71 [==============================] - 21s 298ms/step - loss: 0.7806 - accuracy: 0.7874 - val_loss: 4.2584 - val_accuracy: 0.3737 - lr: 0.0100
Epoch 23/100
71/71 [==============================] - 21s 297ms/step - loss: 0.7751 - accuracy: 0.7972 - val_loss: 1.8139 - val_accuracy: 0.5237 - lr: 0.0100
Epoch 24/100
71/71 [==============================] - 21s 297ms/step - loss: 0.7703 - accuracy: 0.7986 - val_loss: 17.0025 - val_accuracy: 0.2933 - lr: 0.0100
Epoch 25/100
71/71 [==============================] - 21s 298ms/step - loss: 0.7250 - accuracy: 0.8161 - val_loss: 30.8819 - val_accuracy: 0.1753 - lr: 0.0100
Epoch 26/100
71/71 [==============================] - 21s 297ms/step - loss: 0.7151 - accuracy: 0.8183 - val_loss: 0.9915 - val_accuracy: 0.7097 - lr: 0.0100
Epoch 27/100
71/71 [==============================] - 21s 297ms/step - loss: 0.6937 - accuracy: 0.8247 - val_loss: 3.8129 - val_accuracy: 0.3653 - lr: 0.0100
Epoch 28/100
71/71 [==============================] - 21s 298ms/step - loss: 0.6698 - accuracy: 0.8344 - val_loss: 2.4241 - val_accuracy: 0.4927 - lr: 0.0100
Epoch 29/100
71/71 [==============================] - 21s 297ms/step - loss: 0.6499 - accuracy: 0.8454 - val_loss: 4.8920 - val_accuracy: 0.2383 - lr: 0.0100
Epoch 30/100
71/71 [==============================] - 21s 298ms/step - loss: 0.6237 - accuracy: 0.8494 - val_loss: 1.4545 - val_accuracy: 0.6350 - lr: 0.0100
Epoch 31/100
71/71 [==============================] - 21s 297ms/step - loss: 0.6161 - accuracy: 0.8554 - val_loss: 4.6006 - val_accuracy: 0.4037 - lr: 9.9800e-04
Epoch 32/100
71/71 [==============================] - 21s 298ms/step - loss: 0.6056 - accuracy: 0.8556 - val_loss: 2.4397 - val_accuracy: 0.4427 - lr: 0.0100
Epoch 33/100
71/71 [==============================] - 21s 298ms/step - loss: 0.5744 - accuracy: 0.8673 - val_loss: 19.1468 - val_accuracy: 0.3710 - lr: 0.0100
Epoch 34/100
71/71 [==============================] - 21s 298ms/step - loss: 0.5622 - accuracy: 0.8741 - val_loss: 2.9263 - val_accuracy: 0.5177 - lr: 0.0100
Epoch 35/100
71/71 [==============================] - 21s 297ms/step - loss: 0.5324 - accuracy: 0.8912 - val_loss: 6.8771 - val_accuracy: 0.2710 - lr: 0.0100
Epoch 36/100
71/71 [==============================] - 21s 298ms/step - loss: 0.5242 - accuracy: 0.8928 - val_loss: 3.3604 - val_accuracy: 0.3943 - lr: 9.9728e-04
Epoch 37/100
71/71 [==============================] - 21s 297ms/step - loss: 0.5219 - accuracy: 0.8931 - val_loss: 5.1293 - val_accuracy: 0.4177 - lr: 0.0100
Epoch 38/100
71/71 [==============================] - 21s 298ms/step - loss: 0.5024 - accuracy: 0.8974 - val_loss: 17.4596 - val_accuracy: 0.3310 - lr: 0.0100
Epoch 39/100
71/71 [==============================] - 21s 297ms/step - loss: 0.5013 - accuracy: 0.8985 - val_loss: 12.5980 - val_accuracy: 0.1900 - lr: 0.0100
Epoch 40/100
71/71 [==============================] - 21s 298ms/step - loss: 0.5035 - accuracy: 0.8958 - val_loss: 15.6311 - val_accuracy: 0.1513 - lr: 0.0100
Epoch 41/100
71/71 [==============================] - 21s 298ms/step - loss: 0.4757 - accuracy: 0.9091 - val_loss: 1.6949 - val_accuracy: 0.6150 - lr: 9.9645e-04
Epoch 42/100
71/71 [==============================] - 21s 298ms/step - loss: 0.4623 - accuracy: 0.9118 - val_loss: 4.5378 - val_accuracy: 0.3847 - lr: 0.0100
Epoch 43/100
71/71 [==============================] - 21s 298ms/step - loss: 0.4635 - accuracy: 0.9091 - val_loss: 2.6324 - val_accuracy: 0.5700 - lr: 0.0100
Epoch 44/100
71/71 [==============================] - 21s 298ms/step - loss: 0.4494 - accuracy: 0.9167 - val_loss: 9.3926 - val_accuracy: 0.3417 - lr: 0.0100
Epoch 45/100
71/71 [==============================] - 21s 297ms/step - loss: 0.4594 - accuracy: 0.9123 - val_loss: 1.5170 - val_accuracy: 0.6760 - lr: 0.0100
Epoch 46/100
71/71 [==============================] - 21s 297ms/step - loss: 0.4258 - accuracy: 0.9229 - val_loss: 3.6279 - val_accuracy: 0.4643 - lr: 9.9551e-04
Epoch 47/100
71/71 [==============================] - 21s 298ms/step - loss: 0.4279 - accuracy: 0.9267 - val_loss: 1.5515 - val_accuracy: 0.6410 - lr: 0.0100
Epoch 48/100
71/71 [==============================] - 21s 297ms/step - loss: 0.4128 - accuracy: 0.9309 - val_loss: 2.6200 - val_accuracy: 0.6563 - lr: 0.0100
Epoch 49/100
71/71 [==============================] - 21s 297ms/step - loss: 0.4010 - accuracy: 0.9338 - val_loss: 1.3411 - val_accuracy: 0.7097 - lr: 0.0099
Epoch 50/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3892 - accuracy: 0.9348 - val_loss: 2.0966 - val_accuracy: 0.5290 - lr: 0.0099
Epoch 51/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3949 - accuracy: 0.9337 - val_loss: 13.3595 - val_accuracy: 0.3410 - lr: 9.9446e-04
Epoch 52/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3804 - accuracy: 0.9423 - val_loss: 2.2609 - val_accuracy: 0.5503 - lr: 0.0099
Epoch 53/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3976 - accuracy: 0.9312 - val_loss: 12.2866 - val_accuracy: 0.3803 - lr: 0.0099
Epoch 54/100
71/71 [==============================] - 21s 298ms/step - loss: 0.3768 - accuracy: 0.9428 - val_loss: 2.0959 - val_accuracy: 0.6230 - lr: 0.0099
Epoch 55/100
71/71 [==============================] - 21s 298ms/step - loss: 0.3726 - accuracy: 0.9455 - val_loss: 0.8176 - val_accuracy: 0.7397 - lr: 0.0099
Epoch 56/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3763 - accuracy: 0.9389 - val_loss: 0.5371 - val_accuracy: 0.8350 - lr: 0.0099
Epoch 57/100
71/71 [==============================] - 21s 298ms/step - loss: 0.3564 - accuracy: 0.9503 - val_loss: 3.3384 - val_accuracy: 0.4030 - lr: 0.0099
Epoch 58/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3602 - accuracy: 0.9471 - val_loss: 1.8329 - val_accuracy: 0.6067 - lr: 0.0099
Epoch 59/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3504 - accuracy: 0.9513 - val_loss: 18.0323 - val_accuracy: 0.2487 - lr: 0.0099
Epoch 60/100
71/71 [==============================] - 21s 298ms/step - loss: 0.3434 - accuracy: 0.9538 - val_loss: 0.3174 - val_accuracy: 0.9133 - lr: 0.0099
Epoch 61/100
71/71 [==============================] - 21s 298ms/step - loss: 0.3355 - accuracy: 0.9559 - val_loss: 35.9723 - val_accuracy: 0.0870 - lr: 0.0099
Epoch 62/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3407 - accuracy: 0.9556 - val_loss: 4.2022 - val_accuracy: 0.5183 - lr: 0.0099
Epoch 63/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3575 - accuracy: 0.9476 - val_loss: 1.4461 - val_accuracy: 0.6467 - lr: 0.0099
Epoch 64/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3438 - accuracy: 0.9553 - val_loss: 0.5533 - val_accuracy: 0.8570 - lr: 0.0099
Epoch 65/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3289 - accuracy: 0.9586 - val_loss: 2.6462 - val_accuracy: 0.6397 - lr: 9.9094e-04
Epoch 66/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3370 - accuracy: 0.9570 - val_loss: 3.0382 - val_accuracy: 0.6277 - lr: 0.0099
Epoch 67/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3067 - accuracy: 0.9664 - val_loss: 0.4394 - val_accuracy: 0.8750 - lr: 0.0099
Epoch 68/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3374 - accuracy: 0.9523 - val_loss: 0.6390 - val_accuracy: 0.8030 - lr: 0.0099
Epoch 69/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3092 - accuracy: 0.9662 - val_loss: 9.1334 - val_accuracy: 0.3397 - lr: 0.0099
Epoch 70/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3139 - accuracy: 0.9642 - val_loss: 1.9721 - val_accuracy: 0.6420 - lr: 9.8947e-04
Epoch 71/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3023 - accuracy: 0.9661 - val_loss: 6.1290 - val_accuracy: 0.4450 - lr: 0.0099
Epoch 72/100
71/71 [==============================] - 21s 298ms/step - loss: 0.3141 - accuracy: 0.9636 - val_loss: 0.5771 - val_accuracy: 0.8557 - lr: 0.0099
Epoch 73/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2987 - accuracy: 0.9699 - val_loss: 2.3711 - val_accuracy: 0.6177 - lr: 0.0099
Epoch 74/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2987 - accuracy: 0.9662 - val_loss: 2.0595 - val_accuracy: 0.5597 - lr: 0.0099
Epoch 75/100
71/71 [==============================] - 21s 297ms/step - loss: 0.3051 - accuracy: 0.9665 - val_loss: 1.8227 - val_accuracy: 0.6483 - lr: 9.8790e-04
Epoch 76/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2865 - accuracy: 0.9709 - val_loss: 0.2911 - val_accuracy: 0.9123 - lr: 0.0099
Epoch 77/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2879 - accuracy: 0.9711 - val_loss: 7.9214 - val_accuracy: 0.3390 - lr: 0.0099
Epoch 78/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2853 - accuracy: 0.9729 - val_loss: 5.8425 - val_accuracy: 0.4823 - lr: 0.0099
Epoch 79/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2827 - accuracy: 0.9753 - val_loss: 4.8461 - val_accuracy: 0.5063 - lr: 0.0099
Epoch 80/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2770 - accuracy: 0.9743 - val_loss: 0.3016 - val_accuracy: 0.9110 - lr: 0.0099
Epoch 81/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2844 - accuracy: 0.9723 - val_loss: 1.1339 - val_accuracy: 0.7227 - lr: 9.8587e-04
Epoch 82/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2800 - accuracy: 0.9731 - val_loss: 3.2300 - val_accuracy: 0.5413 - lr: 0.0099
Epoch 83/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2761 - accuracy: 0.9753 - val_loss: 1.9819 - val_accuracy: 0.6553 - lr: 0.0099
Epoch 84/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2685 - accuracy: 0.9786 - val_loss: 3.7688 - val_accuracy: 0.5373 - lr: 0.0098
Epoch 85/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2762 - accuracy: 0.9746 - val_loss: 2.0127 - val_accuracy: 0.5893 - lr: 0.0098
Epoch 86/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2799 - accuracy: 0.9742 - val_loss: 0.3813 - val_accuracy: 0.8827 - lr: 9.8405e-04
Epoch 87/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2796 - accuracy: 0.9742 - val_loss: 0.6794 - val_accuracy: 0.8417 - lr: 0.0098
Epoch 88/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2682 - accuracy: 0.9774 - val_loss: 0.3765 - val_accuracy: 0.8830 - lr: 0.0098
Epoch 89/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2699 - accuracy: 0.9760 - val_loss: 2.0573 - val_accuracy: 0.6023 - lr: 0.0098
Epoch 90/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2800 - accuracy: 0.9726 - val_loss: 0.9824 - val_accuracy: 0.7303 - lr: 0.0098
Epoch 91/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2698 - accuracy: 0.9767 - val_loss: 0.5290 - val_accuracy: 0.8860 - lr: 9.8214e-04
Epoch 92/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2588 - accuracy: 0.9802 - val_loss: 1.7468 - val_accuracy: 0.7230 - lr: 0.0098
Epoch 93/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2648 - accuracy: 0.9770 - val_loss: 28.9162 - val_accuracy: 0.2887 - lr: 0.0098
Epoch 94/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2708 - accuracy: 0.9755 - val_loss: 9.8529 - val_accuracy: 0.3633 - lr: 0.0098
Epoch 95/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2617 - accuracy: 0.9790 - val_loss: 1.0323 - val_accuracy: 0.7523 - lr: 0.0098
Epoch 96/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2777 - accuracy: 0.9735 - val_loss: 0.5969 - val_accuracy: 0.8337 - lr: 9.8011e-04
Epoch 97/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2554 - accuracy: 0.9806 - val_loss: 1.3256 - val_accuracy: 0.6893 - lr: 0.0098
Epoch 98/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2541 - accuracy: 0.9805 - val_loss: 0.6708 - val_accuracy: 0.7940 - lr: 0.0098
Epoch 99/100
71/71 [==============================] - 21s 298ms/step - loss: 0.2522 - accuracy: 0.9812 - val_loss: 10.5974 - val_accuracy: 0.2490 - lr: 0.0098
Epoch 100/100
71/71 [==============================] - 21s 297ms/step - loss: 0.2640 - accuracy: 0.9765 - val_loss: 7.4060 - val_accuracy: 0.4123 - lr: 0.0098
fig_cnn2_clr()
pd.Series(results_cnn2_clr)
import pandas as pd
data = {
'Model Name': [
'CNN basic one', 'CNN2 basic one', 'VGG_Baseline basic one', 'CNN basic two',
'CNN2 basic two', 'VGG_Baseline basic two', 'CNN cutmix', 'CNN2 cutmix',
'VGG_Baseline cutmix', 'CNN cutout', 'CNN2 cutout', 'VGG_Baseline cutout',
'CNN_Reg', 'CNN2_Reg'
],
'Epochs': [38, 53, 63, 24, 77, 98, 42, 52, 71, 44, 45, 80, 44, 78],
'Batch Size': [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128],
'Train Loss': [0.017855, 0.015596, 0.169651, 0.228279, 0.038791, 0.144072, 0.164451,
0.157783, 0.238155, 0.009736, 0.042143, 0.129479, 0.881994, 0.307886],
'Test Loss': [0.623409, 0.062343, 0.240200, 0.786181, 0.089882, 0.220837, 0.178881,
0.055629, 0.141491, 0.181197, 0.069994, 0.174903, 0.921476, 0.334669],
'Train Acc': [0.996677, 0.999668, 0.996788, 0.941294, 0.997009, 0.999778, 0.998892,
0.999335, 0.999889, 0.997674, 0.994019, 0.999225, 0.995126, 0.985711],
'Test Acc': [0.840000, 0.982667, 0.974000, 0.758333, 0.979000, 0.976333, 0.945667,
0.992333, 0.994333, 0.951667, 0.984000, 0.986333, 0.964667, 0.970333],
'Kappa': [0.839286, 0.985000, 0.972500, 0.745714, 0.979286, 0.974643, 0.951071,
0.990714, 0.991429, 0.956429, 0.981786, 0.983214, 0.964643, 0.967143],
'Comments': [None, None, None, None, None, None, None, None, None, None, None, None, 'Na', 'Na']
}
overall = pd.DataFrame(overall)
print(overall)